{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
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       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
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       "      <td>L</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
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       "      <td>Father</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bad</td>\n",
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       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>MiddleSchool</td>\n",
       "      <td>G-07</td>\n",
       "      <td>A</td>\n",
       "      <td>Math</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M</td>\n",
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       "      <td>KuwaIT</td>\n",
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       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>F</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>MiddleSchool</td>\n",
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       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>F</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>MiddleSchool</td>\n",
       "      <td>G-07</td>\n",
       "      <td>B</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender NationalITy PlaceofBirth       StageID GradeID SectionID Topic  \\\n",
       "0      M          KW       KuwaIT    lowerlevel    G-04         A    IT   \n",
       "1      M          KW       KuwaIT    lowerlevel    G-04         A    IT   \n",
       "2      M          KW       KuwaIT    lowerlevel    G-04         A    IT   \n",
       "3      M          KW       KuwaIT    lowerlevel    G-04         A    IT   \n",
       "4      M          KW       KuwaIT    lowerlevel    G-04         A    IT   \n",
       "5      F          KW       KuwaIT    lowerlevel    G-04         A    IT   \n",
       "6      M          KW       KuwaIT  MiddleSchool    G-07         A  Math   \n",
       "7      M          KW       KuwaIT  MiddleSchool    G-07         A  Math   \n",
       "8      F          KW       KuwaIT  MiddleSchool    G-07         A  Math   \n",
       "9      F          KW       KuwaIT  MiddleSchool    G-07         B    IT   \n",
       "\n",
       "  Semester Relation  raisedhands  VisITedResources  AnnouncementsView  \\\n",
       "0        F   Father           15                16                  2   \n",
       "1        F   Father           20                20                  3   \n",
       "2        F   Father           10                 7                  0   \n",
       "3        F   Father           30                25                  5   \n",
       "4        F   Father           40                50                 12   \n",
       "5        F   Father           42                30                 13   \n",
       "6        F   Father           35                12                  0   \n",
       "7        F   Father           50                10                 15   \n",
       "8        F   Father           12                21                 16   \n",
       "9        F   Father           70                80                 25   \n",
       "\n",
       "   Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "0          20                   Yes                     Good   \n",
       "1          25                   Yes                     Good   \n",
       "2          30                    No                      Bad   \n",
       "3          35                    No                      Bad   \n",
       "4          50                    No                      Bad   \n",
       "5          70                   Yes                      Bad   \n",
       "6          17                    No                      Bad   \n",
       "7          22                   Yes                     Good   \n",
       "8          50                   Yes                     Good   \n",
       "9          70                   Yes                     Good   \n",
       "\n",
       "  StudentAbsenceDays Class  \n",
       "0            Under-7     M  \n",
       "1            Under-7     M  \n",
       "2            Above-7     L  \n",
       "3            Above-7     L  \n",
       "4            Above-7     M  \n",
       "5            Above-7     M  \n",
       "6            Above-7     L  \n",
       "7            Under-7     M  \n",
       "8            Under-7     M  \n",
       "9            Under-7     M  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据可视化\n",
    "df=pd.read_csv('../csv/StudentPerformance.csv')  # 读取数据\n",
    "df.head(10)                                      # 现在查看十行数据\n",
    "#将所有数据放进模型中进行模型训练，字符无法处理，必须转换为数字数值才能处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    305\n",
       "F    175\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts() # 查看性别的数量，总数480"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    211\n",
       "H    142\n",
       "L    127\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()  # 查看三种等级数，总数480"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                      0\n",
       "NationalITy                 0\n",
       "PlaceofBirth                0\n",
       "StageID                     0\n",
       "GradeID                     0\n",
       "SectionID                   0\n",
       "Topic                       0\n",
       "Semester                    0\n",
       "Relation                    0\n",
       "raisedhands                 0\n",
       "VisITedResources            0\n",
       "AnnouncementsView           0\n",
       "Discussion                  0\n",
       "ParentAnsweringSurvey       0\n",
       "ParentschoolSatisfaction    0\n",
       "StudentAbsenceDays          0\n",
       "Class                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 针对所有的数据看是否有为空的数据，因为有为空的数据我们就要去想办法填充或者解决\n",
    "df.isnull().sum() # 查看数据是否缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                      object\n",
       "NationalITy                 object\n",
       "PlaceofBirth                object\n",
       "StageID                     object\n",
       "GradeID                     object\n",
       "SectionID                   object\n",
       "Topic                       object\n",
       "Semester                    object\n",
       "Relation                    object\n",
       "raisedhands                  int64\n",
       "VisITedResources             int64\n",
       "AnnouncementsView            int64\n",
       "Discussion                   int64\n",
       "ParentAnsweringSurvey       object\n",
       "ParentschoolSatisfaction    object\n",
       "StudentAbsenceDays          object\n",
       "Class                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes \n",
    "# 检查数据类型，因为有些数据是离散型数据，需要和字符型字段区分开来\n",
    "# 由此可得，只有四个数值型，其他的都是字符型，这时候要对这些字符型进行特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.775000</td>\n",
       "      <td>54.797917</td>\n",
       "      <td>37.918750</td>\n",
       "      <td>43.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.779223</td>\n",
       "      <td>33.080007</td>\n",
       "      <td>26.611244</td>\n",
       "      <td>27.637735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       raisedhands  VisITedResources  AnnouncementsView  Discussion\n",
       "count   480.000000        480.000000         480.000000  480.000000\n",
       "mean     46.775000         54.797917          37.918750   43.283333\n",
       "std      30.779223         33.080007          26.611244   27.637735\n",
       "min       0.000000          0.000000           0.000000    1.000000\n",
       "25%      15.750000         20.000000          14.000000   20.000000\n",
       "50%      50.000000         65.000000          33.000000   39.000000\n",
       "75%      75.000000         84.000000          58.000000   70.000000\n",
       "max     100.000000         99.000000          98.000000   99.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # 不加参数是默认对数据中的整型去做描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>raisedhands</th>\n",
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       "      <th>ParentschoolSatisfaction</th>\n",
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       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480</td>\n",
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       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>MiddleSchool</td>\n",
       "      <td>G-02</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>305</td>\n",
       "      <td>179</td>\n",
       "      <td>180</td>\n",
       "      <td>248</td>\n",
       "      <td>147</td>\n",
       "      <td>283</td>\n",
       "      <td>95</td>\n",
       "      <td>245</td>\n",
       "      <td>283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>270</td>\n",
       "      <td>292</td>\n",
       "      <td>289</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.775000</td>\n",
       "      <td>54.797917</td>\n",
       "      <td>37.918750</td>\n",
       "      <td>43.283333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.779223</td>\n",
       "      <td>33.080007</td>\n",
       "      <td>26.611244</td>\n",
       "      <td>27.637735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender NationalITy PlaceofBirth       StageID GradeID SectionID Topic  \\\n",
       "count     480         480          480           480     480       480   480   \n",
       "unique      2          14           14             3      10         3    12   \n",
       "top         M          KW       KuwaIT  MiddleSchool    G-02         A    IT   \n",
       "freq      305         179          180           248     147       283    95   \n",
       "mean      NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "std       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "min       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "25%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "50%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "75%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "max       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "\n",
       "       Semester Relation  raisedhands  VisITedResources  AnnouncementsView  \\\n",
       "count       480      480   480.000000        480.000000         480.000000   \n",
       "unique        2        2          NaN               NaN                NaN   \n",
       "top           F   Father          NaN               NaN                NaN   \n",
       "freq        245      283          NaN               NaN                NaN   \n",
       "mean        NaN      NaN    46.775000         54.797917          37.918750   \n",
       "std         NaN      NaN    30.779223         33.080007          26.611244   \n",
       "min         NaN      NaN     0.000000          0.000000           0.000000   \n",
       "25%         NaN      NaN    15.750000         20.000000          14.000000   \n",
       "50%         NaN      NaN    50.000000         65.000000          33.000000   \n",
       "75%         NaN      NaN    75.000000         84.000000          58.000000   \n",
       "max         NaN      NaN   100.000000         99.000000          98.000000   \n",
       "\n",
       "        Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "count   480.000000                   480                      480   \n",
       "unique         NaN                     2                        2   \n",
       "top            NaN                   Yes                     Good   \n",
       "freq           NaN                   270                      292   \n",
       "mean     43.283333                   NaN                      NaN   \n",
       "std      27.637735                   NaN                      NaN   \n",
       "min       1.000000                   NaN                      NaN   \n",
       "25%      20.000000                   NaN                      NaN   \n",
       "50%      39.000000                   NaN                      NaN   \n",
       "75%      70.000000                   NaN                      NaN   \n",
       "max      99.000000                   NaN                      NaN   \n",
       "\n",
       "       StudentAbsenceDays Class  \n",
       "count                 480   480  \n",
       "unique                  2     3  \n",
       "top               Under-7     M  \n",
       "freq                  289   211  \n",
       "mean                  NaN   NaN  \n",
       "std                   NaN   NaN  \n",
       "min                   NaN   NaN  \n",
       "25%                   NaN   NaN  \n",
       "50%                   NaN   NaN  \n",
       "75%                   NaN   NaN  \n",
       "max                   NaN   NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all') # 查看数据详细信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.775000</td>\n",
       "      <td>54.797917</td>\n",
       "      <td>37.918750</td>\n",
       "      <td>43.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.779223</td>\n",
       "      <td>33.080007</td>\n",
       "      <td>26.611244</td>\n",
       "      <td>27.637735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       raisedhands  VisITedResources  AnnouncementsView  Discussion\n",
       "count   480.000000        480.000000         480.000000  480.000000\n",
       "mean     46.775000         54.797917          37.918750   43.283333\n",
       "std      30.779223         33.080007          26.611244   27.637735\n",
       "min       0.000000          0.000000           0.000000    1.000000\n",
       "25%      15.750000         20.000000          14.000000   20.000000\n",
       "50%      50.000000         65.000000          33.000000   39.000000\n",
       "75%      75.000000         84.000000          58.000000   70.000000\n",
       "max     100.000000         99.000000          98.000000   99.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=[np.int64]) # 查看整形数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#修改列名\n",
    "df.rename(index=str,columns={\n",
    "    'gender':'Gender',\n",
    "    'NationalITy':'Nationality',\n",
    "    'raisedhands':'Raisedhands',\n",
    "    'VisITedResources':'VisitedResources'\n",
    "},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>VisitedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Nationality PlaceofBirth     StageID GradeID SectionID Topic  \\\n",
       "0      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "1      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "2      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "3      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "4      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "\n",
       "  Semester Relation  Raisedhands  VisitedResources  AnnouncementsView  \\\n",
       "0        F   Father           15                16                  2   \n",
       "1        F   Father           20                20                  3   \n",
       "2        F   Father           10                 7                  0   \n",
       "3        F   Father           30                25                  5   \n",
       "4        F   Father           40                50                 12   \n",
       "\n",
       "   Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "0          20                   Yes                     Good   \n",
       "1          25                   Yes                     Good   \n",
       "2          30                    No                      Bad   \n",
       "3          35                    No                      Bad   \n",
       "4          50                    No                      Bad   \n",
       "\n",
       "  StudentAbsenceDays Class  \n",
       "0            Under-7     M  \n",
       "1            Under-7     M  \n",
       "2            Above-7     L  \n",
       "3            Above-7     L  \n",
       "4            Above-7     M  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()  # 查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='count'>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns # 绘制成绩等级条形图\n",
    "sns.countplot(x='Class',order=['L','M','H'],data=df) # 检查数据集是否平衡,order参数是绘制图像的先后顺序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PlaceofBirth', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEGCAYAAABhMDI9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeP0lEQVR4nO3deZhcVZ3G8e+bBAGFsKWJyNaAER50MEAPgiyyOLKIIohIREwEDTggozOu6CPoPIwrMqMsThAM+xo2EQVEAdnpQAgJ+xIkGUiaRSABkYTf/HFO0fdWurqrk6qu7vT7eZ566tapW+eeunXu+d1zl1OKCMzMzCpGtLoAZmY2uDgwmJlZiQODmZmVODCYmVmJA4OZmZWManUBlseYMWOivb291cUwMxtSpk+f/lxEtNV6f0gHhvb2djo7O1tdDDOzIUXSU72970NJZmZW4sBgZmYlDgxmZlbiwGBmZiUODGZmVuLAYGZmJQ4MZmZW4sBgZmYlDgxmZlYypO98Luo67dyG5dX2pc82LC8zs6HGPQYzMytpWmCQdKakBZJmFdIukjQjP+ZImpHT2yW9VnjvV80ql5mZ9a6Zh5KmAicDZ1cSIuLTlWlJJwIvFeZ/PCLGN7E8ZmZWh6YFhoi4WVJ7T+9JEnAQsHuzlm8rjr2vnNiwvH6/31kNy8tsRdWqcww7A/Mj4tFC2iaS7pV0k6Sda31Q0mRJnZI6u7q6ml9SM7NhplWBYQJwQeH1M8BGEbE18O/A+ZJG9/TBiJgSER0R0dHWVvN/JszMbBkNeGCQNAo4ALiokhYRr0fE83l6OvA48J6BLpuZmbWmx/Bh4KGImFtJkNQmaWSe3hQYBzzRgrKZmQ17zbxc9QLgdmBzSXMlHZ7fOpjyYSSAXYCZ+fLVS4EjI+KFZpXNzMxqa+ZVSRNqpE/qIW0aMK1ZZTEzs/r5zmczMytxYDAzsxIHBjMzK3FgMDOzEgcGMzMrcWAwM7MSBwYzMytxYDAzsxIHBjMzK3FgMDOzEgcGMzMrcWAwM7MSBwYzMytxYDAzsxIHBjMzK3FgMDOzEgcGMzMrcWAwM7MSBwYzMytxYDAzs5KmBQZJZ0paIGlWIe14SfMkzciPfQrvfVvSY5IelrRns8plZma9a2aPYSqwVw/pJ0XE+Py4BkDSlsDBwHvzZ06VNLKJZTMzsxqaFhgi4mbghTpn3w+4MCJej4gngceA7ZpVNjMzq60V5xiOljQzH2paK6etDzxdmGduTluKpMmSOiV1dnV1NbusZmbDzkAHhtOAzYDxwDPAif3NICKmRERHRHS0tbU1uHhmZjaggSEi5kfEkoh4Ezid7sNF84ANC7NukNPMzGyADWhgkLRe4eX+QOWKpauAgyWtLGkTYBxw10CWzczMklHNyljSBcCuwBhJc4HjgF0ljQcCmAMcARARsyVdDDwALAaOioglzSqbmZnV1rTAEBETekg+o5f5TwBOaFZ5zMysPr7z2czMShwYzMysxIHBzMxKHBjMzKzEgcHMzEocGMzMrMSBwczMShwYzMysxIHBzMxKHBjMzKzEgcHMzEocGMzMrMSBwczMShwYzMysxIHBzMxKHBjMzKzEgcHMzEocGMzMrMSBwczMShwYzMyspGmBQdKZkhZImlVI+6mkhyTNlHS5pDVzeruk1yTNyI9fNatcZmbWu2b2GKYCe1WlXQ+8LyK2Ah4Bvl147/GIGJ8fRzaxXGZm1oumBYaIuBl4oSrtuohYnF/eAWzQrOWbmdmyaeU5hsOA3xdebyLpXkk3Sdq51ockTZbUKamzq6ur+aU0MxtmWhIYJH0HWAycl5OeATaKiK2BfwfOlzS6p89GxJSI6IiIjra2toEpsJnZMDLggUHSJGBf4JCICICIeD0ins/T04HHgfcMdNnMzGyAA4OkvYBvAB+PiFcL6W2SRubpTYFxwBMDWTYzM0tGNStjSRcAuwJjJM0FjiNdhbQycL0kgDvyFUi7AD+Q9AbwJnBkRLzQY8ZmZtZUTQsMETGhh+Qzasw7DZjWrLKYmVn9fOezmZmVODCYmVmJA4OZmZU4MJiZWYkDg5mZlTgwmJlZiQODmZmVODCYmVmJA4OZmZU4MJiZWYkDg5mZlTgwmJlZiQODmZmVODCYmVmJA4OZmZU4MJiZWYkDg5mZlTgwmJlZSV2BQdIN9aSZmdnQ1+t/PktaBXg7MEbSWoDyW6OB9ZtcNjMza4G+egxHANOBLfJz5XElcHJfmUs6U9ICSbMKaWtLul7So/l5rZwuSb+Q9JikmZK2WdYvZWZmy67XwBAR/xMRmwBfi4hNI2KT/Hh/RPQZGICpwF5Vad8CboiIccAN+TXA3sC4/JgMnNaP72FmZg3S66Gkioj4paQPAu3Fz0TE2X187mZJ7VXJ+wG75umzgBuBb+b0syMigDskrSlpvYh4pp4ymplZY9QVGCSdA2wGzACW5OQAeg0MNYwtNPbPAmPz9PrA04X55ua0UmCQNJnUo2CjjTZahsWbmVlv6goMQAewZd6bb5iICEn9yjMipgBTADo6OhpaHjMzq/8+hlnAOxu0zPmS1gPIzwty+jxgw8J8G+Q0MzMbQPUGhjHAA5KulXRV5bGMy7wKmJinJ5KucKqkfy5fnbQ98JLPL5iZDbx6DyUdvyyZS7qAdKJ5jKS5wHHAj4CLJR0OPAUclGe/BtgHeAx4Ffj8sizTzMyWT71XJd20LJlHxIQab+3Rw7wBHLUsyzEzs8ap96qkV0hXIQG8DVgJWBQRo5tVMDMza416ewyrV6YliXTPwfbNKpSZmbVOv0dXjeQKYM/GF8fMzFqt3kNJBxRejiDd1/D3ppTIzMxaqt6rkj5WmF4MzCEdTjIzsxVMvecYfOmomdkwUe8f9Wwg6fI8hPYCSdMkbdDswpmZ2cCr9+Tzb0h3Jr8rP36b08zMbAVTb2Boi4jfRMTi/JgKtDWxXGZm1iL1BobnJX1W0sj8+CzwfDMLZmZmrVFvYDiMNKbRs6T/RzgQmNSkMpmZWQvVe7nqD4CJEfEipP9tBn5GChhmZrYCqbfHsFUlKABExAvA1s0pkpmZtVK9gWGEpLUqL3KPod7ehpmZDSH1Nu4nArdLuiS//hRwQnOKZGZmrVTvnc9nS+oEds9JB0TEA80rlpmZtUrdh4NyIHAwMDNbwfV72G0zM1uxOTCYmVnJgF9ZJGlz4KJC0qbA94A1gS8CXTn92Ii4ZmBLZ2ZmAx4YIuJhYDyApJHAPOBy4PPASRHxs4Euk5mZdWv1oaQ9gMcj4qkWl8PMzLJWB4aDgQsKr4+WNFPSmcUb6szMbOC0LDBIehvwcaBy09xpwGakw0zPkG6q6+lzkyV1Surs6urqaRYzM1sOrewx7A3cExHzASJifkQsiYg3gdOB7Xr6UERMiYiOiOhoa/NfQpiZNVorA8MECoeRJK1XeG9/YNaAl8jMzFozEJ6kdwD/AhxRSP6JpPFAAHOq3jMzswHSksAQEYuAdarSDm1FWczMrKzVVyWZmdkg48BgZmYlDgxmZlbiwGBmZiUODGZmVuLAYGZmJQ4MZmZW4sBgZmYlDgxmZlbiwGBmZiUODGZmVuLAYGZmJQ4MZmZW4sBgZmYlDgxmZlbiwGBmZiUODGZmVuLAYGZmJQ4MZmZW4sBgZmYlo1q1YElzgFeAJcDiiOiQtDZwEdAOzAEOiogXW1VGM7PhqNU9ht0iYnxEdOTX3wJuiIhxwA35tZmZDaBWB4Zq+wFn5emzgE+0rihmZsNTKwNDANdJmi5pck4bGxHP5OlngbHVH5I0WVKnpM6urq6BKquZ2bDRsnMMwE4RMU/SusD1kh4qvhkRISmqPxQRU4ApAB0dHUu9b2Zmy6dlPYaImJefFwCXA9sB8yWtB5CfF7SqfGZmw1VLAoOkd0havTINfASYBVwFTMyzTQSubEX5zMyGs1YdShoLXC6pUobzI+IPku4GLpZ0OPAUcFCLymdmNmy1JDBExBPA+3tIfx7YY+BLZGZmFYPtclUzM2sxBwYzMytxYDAzsxIHBjMzK3FgMDOzEgcGMzMrcWAwM7MSBwYzMytxYDAzsxIHBjMzK3FgMDOzEgcGMzMrcWAwM7MSBwYzMytxYDAzsxIHBjMzK3FgMDOzEgcGMzMrcWAwM7MSBwYzMysZ8MAgaUNJf5b0gKTZkv4tpx8vaZ6kGfmxz0CXzczMYFQLlrkY+I+IuEfS6sB0Sdfn906KiJ+1oExmZpYNeGCIiGeAZ/L0K5IeBNYf6HKYmVnPWnqOQVI7sDVwZ046WtJMSWdKWqvGZyZL6pTU2dXVNVBFNTMbNloWGCStBkwDvhIRLwOnAZsB40k9ihN7+lxETImIjojoaGtrG6jimpkNGy0JDJJWIgWF8yLiMoCImB8RSyLiTeB0YLtWlM3MbLgb8HMMkgScATwYET8vpK+Xzz8A7A/MGuiymdmK546pCxqW1/aT1m1YXoNZK65K2hE4FLhf0oycdiwwQdJ4IIA5wBEtKJuZ2bDXiquSbgHUw1vXDHRZzMxsab7z2czMSlpxKGlImnfKUQ3La/2jTmlYXmZmjeYeg5mZlTgwmJlZiQ8lDQMXTN2zoflNmHRt6fWPL2xs/t88+Nq+ZzKzpnGPwczMShwYzMysxIHBzMxKHBjMzKzEgcHMzEocGMzMrMSXq5qZLYdnf/pUw/J659c3blhey8OBYZD4y+n7Niyvnb94dcPyssFvv0sbd9/HlQc29p4UG5p8KMnMzEocGMzMrMSBwczMSnyOwazJ9r30vIbldfWBhzQsr8HirMu6GpbXxAPaGpbXYDH/Fzc3LK+xx+xS13zuMZiZWYl7DGbWq4OmPdSwvC7+5BYNy8uaxz0GMzMrGXSBQdJekh6W9Jikb7W6PGZmw82gCgySRgKnAHsDWwITJG3Z2lKZmQ0vg+0cw3bAYxHxBICkC4H9gAdaWipboX30sv9uWF6/O+ArDcvLrFUUEa0uw1skHQjsFRFfyK8PBT4QEUcX5pkMTM4vNwce7scixgDPNai4zt/5O/+By38ol30w5r9xRNS8tnew9Rj6FBFTgCnL8llJnRHR0eAiOX/n7/ybnP9QLvtQzH9QnWMA5gEbFl5vkNPMzGyADLbAcDcwTtImkt4GHAxc1eIymZkNK4PqUFJELJZ0NHAtMBI4MyJmN3ARy3QIyvk7f+ff8vyHctmHXP6D6uSzmZm13mA7lGRmZi3mwGBmZiVDKjBIWliY3kfSI5KW609Si3kW0iZJOlnSdyQ9IOk1SUskzciPY5ZnmXkZxfxmVA//kZc9W9LM/P4H+pn/eEn7VKXdKKkjT18jac3Ce5+QFJKWaZQzSYskzapKO17S9ZK+LOnO/D0elHR81Xz/LWmepBE9/R79KMPUfC/MoCJp4fJ8rxp5rlOoO8/m9Vd5/bY684g89MyW+fUoSV2SBuS/YZenjkv6dX9GRejP+i9sm7MkXSLp7b3MO0nSyfXmXfhcu6TPFF53SPpFf8tbq85X59dfg+rkc70k7QH8AtgzIhr3T9xVIuIESecBVwPtETG+gdm/Vis/STsA+wLbRMTrksYAdW3sBeOBDuCant6MiH2qkiYAt+Tn43oo06iIWNzPMkC6kOBo4KCIuC8Pe7J5Id8RwP7A08CH6s10OcozaCzPd4iI50m/MTnQLoyIn/Uzm0XAQuDJ/Ppf6Ofl4cv6HZanjksaWbkJdnn0Uva3ts28/R8J/Hx5l1elHfgMcD5ARHQCnY3KfLnzi4gh8yBV4l2AJ4Atclo7MKswz9eA44F1gek57f1AABvl148Dbwc+BiwB7gX+CHyfdMns08BdhfwfAt4AHgQuBd6e3/tenn8W6aqAysn8G4EfA3cBjwA75/RVgN8A9+fl7pbTJwGXAX8gbZivAH8jBb+rST27R4EfFZb3EtAGTAX+jzRsyCOkyjYH+CvQBbwIzAUuB+4EOvIy5wBj8vRqebnvAR4urMtdgb+QLhl+JKddAUwHZgOTC/MuIt15ORu4IZft+Fzehfn3WGp9AbuTgtfEnLYwp/80z3c/8OmeypPnO5l09/sfcz4H5vV0dWFZ00n14uvAy8CC/H0fAT6Vf9fTc9mvA1YF3gXMKDyWABvn9X1gsU4Wpr+elzkT+H5VvV2Y1/P0PP0yMK+PdboQOAG4D7gDGFtjuzg+f78ey5bX242kuvsQcF5edwuBp4BvkK4CfAx4Npftq8DauWwz8/K3KizvHOBW4ALSNvKnPN8NdG9nY0n17r78+GBO/xyp/r0EnFPYzu7NaTcAG5EC1eWFdXFizmen/H0qdfk0UiM4u7jeq9uOeut0Yd7PA/Pz49q8biptxdjCtntynm4DppHqwN3Ajjn9Q3TXo3uB1fP6fCmnfTWX6+rC+n0jf8cngH8jbf9twGdJ7crrwFn58av8/R8B9i18z0p+2wG352XfBmzeZ1vb6sa+n4HhDeAFcgXtLTDk6dnAaNIe693AIaSN+/b8/lqFSvBzUsVWrhBPkoJQOymovJrnOxP4Wp5eu7Dcc4CPFQLDiXl6H+CPefo/SJfgQmpo/kGq6H/NlW9dUlCal8v+MmkD+hBpL/7Y/NmPkDboj5EagxeAfwbGkYLEU6QKe0theVsBi+k5MBwCnJGnbwO2LVSuRcAmhe+5dn5eldTorpNfB/B0IWCeTHdguJAUoK4GjiAFyHNy+U8HDs2/0zxSA/BJ4HpSYzU2r5/1qssDHFCY712kYHogsDVwa6HMfwNOIgWeG0kNzNWkBvGWvF7G53kvBj5bVe+OAi7O01PpufH9CN3BbkTOf5eqwDCKVB8WAduQGmL1sU4rdeonwHeXIzC8RLphdASpkdgpl+luUkO8A2mHZNdc9jWBXwLH5Tx2B2YUljcdWDW//i0wMU8fBlyRpy8CvpKnRwJrAO8lNWAbkxrFx4BTSUFmIilwHUNqrM8vfP8g9Tor3+1Guuvy2oVl3EihfaixLvqq0wtJ9e2vwO9J7ccddAeAL9C9fU8qpJ8P7JSnNwIeLKyfSpBYjVQPdiU33IVyFQPDEmBl0lAXz5N2Wn+Y89qbFIBOJQW5P+TfdRxpJ3CVqvxGA6Py9IeBaX21tUPqHAMpMNwGHF7n/LcBO5Ia+P/KzzuTViakDWUVSfeTfuBxpKj6fVLQGJfnexp4M0+fS9qoAHbLx87vJ2047y0s+7L8PJ0UXMifOzdPv0bagz+U1JBeRWrcniAFqC+R9nz/QdrAXgG+IOlOUuO1WmF5C4CIiEdJDf5KOf1dleVFxMycb08mkBpv8vOEwnt3RcSThdfHSKrswW5I9zp6k9T4QHkdQdpT7SA1/D8l9Sx2JwWrfUgNyct5fYzMn70gIpZExHzgJlLgqy7PLoX5/o+010pE3AtsLOkeSY+SNvhtSY13B/BxYAtSfdoAeDIiZuQ8i78XknYEvkhq8Hrzkfy4F7gn5z+uah7lvIK0g7E+KfDVWqf/IDXSS5VrGdwVEXMj4k1Sg1zJaxGpEdyGFCCPIe2Zvkz6Hc4BiIg/AetIGp0/d1VEvJandyAfEsnzV3773Ul78+Tf6KWcdkmkQ8DbkhrZrpzHqPz5lUjb6Q6khhlSQzmtxnc7SNI9pHX/XtLIzH2ti97q9KqktmM1UuCaAvwZ+Hje1r9OeVuv+DBwsqQZpO15tKTVSEHv5/nc5JpR36G3xRHxekQ8R9q+ryb1FrYlreutgT1IvY+LI+LNvP0/Qap7RWsAl+RzgCfVKHvJUAsMbwIHAdtJOjanLab8PVYpTN9MqmAbA1eSDintRHdg+CXwRkT8E2nP86+Rji1+Dzg3Is7I80VVOULSKqSIfWD+/OlVy349Py+hvnM5rxeml+TvNIu0d3U0aS9hfVLP5gXSHvkqVfOTn6vLW5OktUkb668lzSFV+oMkKc+yqDDvrqTKv0NEvJ+0IRa/81qF6SAdivh7fj0P+AQpGLxBCh7vJe2Z3p+XvRN9r6tFfbxP/m3WJO11XkIKGKNIe1ydwISIeDepERrF0ut+VM5nPeAM0p5q5WTgW/Utnx+pHBcX8MOIGJ8f7y7Un4pDSBvpTbmezSft2dVap29E3s2jvnpUq2zU+o7ZzcAPgD1JQWNj4Nd9LKvP36EvOVjcGBHHkfbS9ycdav0MaW/5kkIj+veIWFKdh6RNSL2lPSJiK+B3lOtkr2WvUaf/QTq8c3VEfDki/kHqic7M23ql11ttBLB9oQ6sHxELI+JHpAC4KnDrMlzgsYQUPF8h9YheBMZFxOak32up9qnq9X8Cf46I95F66X2tnyEXGIiIV4GPAodIOpx8CCZfpbEy6YRWxV9IUfbRvKf0AmkP9Zb8/hp0r8Q1gfVyhAd4h6R18/RGdK+rz+TPV1buc/kz9VwN8xdS4wCpIdmI8uiwDwObAuuQgsCnc/r4/N5rpIh/BelwS8VooEPSZqS9hcWkSvR8Li+S3kdqlKsdSDrOu3FEtEfEhqTDaDv3MO8awIsR8Wqu3NsX3hsB/F3S7nmZdwN7kfZ2tqF7fa1JqugfBf4J+EJebjuwCanHcCfwaUkjJbWRegZ39VCemwvzrQfsltNXIQWkfUjnEDYhnVc6LOePpPVJ3fQeSVqJFFS+GRGPFN6aQ9prg9TzqPTOrgUOq9QfSesX6k/FGqTDWiFpN1IDvDq112l/1SpbX64CfkY6jHMzqa5tQ6G+5gb0udyzq3Ybafga8vyVHa8bSD1f8m+0BilIf0rSBySNyzsmkI7fr5x7fiNIv9Nv6ij7aFJD/5KksaQdqP6oVafvBD6U25WVSL2qSkCZWCOv64AvV15IGp+fN4uI+yPix6TtYgvS9rl6P8v6v6Qd499FxJK87t5BWp8j8va/KUuPOL0G3RcVTKpnQUMuMABExAukRue7pIrwA1LDcT2pclfmm0NqgG/OSbcAf4uIF/Pr40mHkqaTDrPMJx1//c+cf+WHexhYSdKDpL3i0yLib6RewixSo3B3HUU/FRiRu6NvJ+0535nLv0Pumv8rqWfz6/zddiR1jY/Pn38naS+zuLxbSRv2bNLxxiB1fQV8UtLcvIzpPZRpAukEYdE0yoeTKv4AjMrr4UekrnfFIrpP6n2TdBjg+6QNoNKwR/7MfNLhlneT9vAqXs/rJEi/x32khuQbEfFsD+W5nHRS7gHgbNJvR/5tfkXqbq+b0x+nuwt+Ienw1mpL5djtg6TDTt8vXAb6LtJv/qF86GGH/L2JiOty/rfn3/dSlt7wzyNdkbUL6QTsQ6TDZLXWaX/1WLY6LCCduL+RVO/GA98m1bltJc3MZavVIH4Z+Hye71DSyVLy8255fUwHtow0xM0JpHUxA3gif+5x0rY4kxTQHoyIB/sqeETcR9rLf4i0/m+t8ztX9FinI+IZ0ve/Ped5K7BXbitqDW99DGkHbaakB0hXMwF8JV/6OpNUv39Pqt9LJN0n6as95LWSpLl5292cdPj8VFJv5sM5r+tJ7chfSdvX74EjI+LvVXn9BPihpHup80pUD4kxyEhaLSIW5kM5p5B6Oyfl9zqAkyJi58L8U0ld3ktbUuAGkvR+4PSI2K7VZbHWUbov4N4eDsW1jKRJpJPdR/c1bxPLsNT23yxDssewgvtiPnk1m9QF/F8ApRvgppH25FY4ko4kXfr43VaXxVon75FvRfdFGsbAb//uMZiZWYl7DGZmVuLAYGZmJQ4MZmZW4sBgw4JqjJipBo962svyV5b0x1yGTyuNdPuwukecnVyYtzTybVU+xxam21U1oq1ZIzgw2HDxWr4b9X2ka8GP7OsDDbY1QC7DRTntkHwH9I7Aj5WHy46IffK9GG9RMgI4FrMmc2Cw4egvpJvr3iJpNUk3KI2vdL+k/QrvfS7ftHSfpHNyWpukaZLuzo8dc/rakq7I898haat8B/S5wD/nHsJmVeVZjXQz2pKcxxxJY3KP4GFJZ5NupDwDWDXncV7+7EhJpyv9r8F1klZt/OqyYaevUfb88GNFeNA9uuYo0rhZX+ohfXSeHkP3yKeV0UArI9FWRuKsNZJmrRFJd6U8muaNpDvqZ5KGOjmi8N6cXIZ20vhg21d/jzzdTh8jw/rhx7I8huQf9Zgtg1XzjYOQegzVd9UK+C9Ju5Aa48rIp5XRQJ+Dt4ZjgTTw2pZ6a6zBt0bS3Ik8jlVE/CmPtTOanh0SEZ1K40HdJukPsfQfTz0VEb0Nk/Fk1BgZ1mxZOTDYcFHzH/OyQ0jDTW8bEW8ojfba2yiUlZE0S+PSFAJF3SKiS2nY6A+Q/kujqK/xjqpHTfWhJFtuPsdglqwBLMhBoTLyKXSPBroOvDVMOdQYSZP6RyR9S75CamvSQHJ9eUNptE+zpnGPwSw5D/htHgm0kzxKb0TMlnQCcJOkyt/ATiKNpHlKHuVyFGkE3yNJI3KemdNfpfaIpADnSXqN9N8DUyOip9Fvq00BZuYexnf6/S3N6uCxkszMrMSHkszMrMSBwczMShwYzMysxIHBzMxKHBjMzKzEgcHMzEocGMzMrOT/AYkZBA23KPMoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='PlaceofBirth',data=df) # 绘制出生地条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='PlaceofBirth'>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PlaceofBirth'].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GradeID', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='GradeID',data=df) # 绘制年纪ID条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制条形图 Semester:f/s\n",
    "plt.figure(figsize=(8,6)) # 图的长宽比例\n",
    "sem=sns.countplot(x='Class',hue='Semester',order=['L','M','H'],data=df,palette='coolwarm') # order表示横轴序列\n",
    "sem.set(xlabel='Class',ylabel='Count',title='Semester comparison') # x、y轴说明\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))  # 同上，此处描述年纪ID\n",
    "sem=sns.countplot(x='Class',hue='GradeID',order=['L','M','H'],data=df,palette='coolwarm')\n",
    "sem.set(xlabel='Class',ylabel='Count',title='GradeID comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='VisitedResources'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VOxfnYVDHQhlVkbh1QS5LytMxGRW+fH8FyXH+K7756TF8qrAF77iAeoSnfh+ug+vQBrsCAmrwJfC5T+zBULYG5HEVOxQjxll34zryTmBHRY+LSdVP8cCNuRgNY3NVZIkVszI5UNsVsIqt6TpPrz1KR5+dd3Y3+QXUIypP9vLq1pNo9gEc63/tF1AD4LLhqdmEccbtoIxbQ1VUX9Jy8WIkowXLmq8gRVv9DpVTCrGs+ELAa15MK2dnYTIED0tNBpmVs7Mu2GstW3YTv/rVLyguLkVVL9z6slipFgRBuIrIksRDK4pYMy+HypO+OtXT8hNRT2zCGaINsu4cwl67ix2VoVeB3q7VmP7Q9/G2VKEPdiHHp/sFy5KsYrnxE2gz78DbUg0GE2pWOZLBdMHfoyCcqdsW5rF0RiaHTnSDDlPyE4iJMI6OZ6dE8/8+PY+qk730DDhIt0ZSkB7L0HPPEaqWhLt6o68Sjh5in/NgB2rWNIxlq/A2HQFZRs0q89WBDtVR0W3nxrhGFn1+IYdO9ODxapTmJtDZZ+ft3U1BD9F0nc0HW4IG1CPeP9DCbQm1gZV7TtFtfchJOUQ+/NPRLxFK1jRkc/TocxRrLpEP/ABvc6WvRn1CJkpyfsjXvFjmlqawu7ojIFnRZJApzU1gTmlKmKPPzsKFS/j+9/+dT3ziMxfsnCCCakEQhKtSfLSJhdPSRv/faQufUOTq78blCb3HtHfIiSRJqBmlYc8jR1uRi61hnyMIl1KUxcD8MFUhZEliSt5YQxhd14NudRodH+6dMEFPH+5FzihFLlo4+thEHRX14T4izAbmjgsOaxpDzwN8yZjhEpAHbW68Z5BsKJujkSctCPkcSZJRM6eGPc/FJksSn79rGjtP1anuGXCQcKpO9ZwLUKc6LS2dv/71VQAiIiJ4990to2Pf/Oa/nte5R4igWhAE4RowUbKgJTWH6IgBBkPsWcxIigz6uCBcayRJQo7PQOttDjouJ2QiJ4S/nqSEDDwN+/E0HARZQc2diTJh0l8mNY197D7agVfTmZaXSHpSRNhjMq1RtPfYqW0OrE0PkJIQgZqYSejaPFdXIrEsScybknrepfMuFxFUC4IgXAPUvBlI0Unog10BY3J8OsbsaSybUc/Lm+sCxiVgxawLt19REK50hmkrcb7/h6BjxmmrULOm4drzctDyeUpqEa5tL+BtrR59zH34bZTcmUixKej9QToqxiTzh4NGtlftHX3svb3NTMqMpSQ7juqGvoBjIkwqC6elkRhjDhlUr5ydhZpvRdr51xCJxjmo6SVBjxUuPJGoKAiCcA2QZJWINV8J7OaWmINl1eNIksxtC3JZOj3D7zaqxaTw4TUllORcmI5ignA1MJbcgHHGHacl8Bkxzr7bV7/dFInl5q8gRfs3h1HSipESMv0C6hHek3sw5M9FTvT/gionZHIw+yG2VwXujT7W1E9yvIWS7Di/xxNjzDx+XzlRFgOzSpK5d2kBxtMSMm+Zn8PS6RlIqhHLzU8gx/qv7srW/NFEY+HSkHT9AvR9vAJ0dw+hadfEWxEEQTgvntajvjrVMSkoqZMCxnsGHFQ39GJUFabmJ2A2ipuWwvVJcwzibToMSKiZU/1qugPouuZL4LP1+baFJOYw9MwXQiYkyvGZRN77XTxtx9AH2pGirahpxfzbH3ZR3x68prbFpPLfjy2msWOIps4hYiONlOYmBHRYHHa4OXyiB13XKc1NICbS6Deu6zre1urROtWXshxeMG1t9aSm5lzWOZyvYO9BliUSE4Pnp4jfpIIgCNcYNa0Y0opDjifEmFkwNS3kuCBcL2RzNHLh/JDjpyfw6R5X6AofgHYqYVhNnQTjvtCG695oP9VRMSc1mpzU6JDPizwtyTFwrlJA8xrh0hJBtSAIggD4VrA3H2qlZ8BBakIki8rSiLJcmPa9gnAtkFQjUkwy+kBH0HElIRPN1u/rwjjQjhSdjKF4EZnWSCpDdlQ0o2k67+5porFjkNhIEwvL0gLqa3uaK/HU7QF01OwylKwyJEns4r2SiO0fgiAIAruqO/jtq0fweMd+j1pMCl+8u4zibLHfWhBGuI68g3PLc0HHjDPuwHXwTfCMC6AVI7Wln+S/Nwbf/rFqThZbD7f5VeaRJHhkVTE3VGSgax4c7/wPnpN7/Y5T0idjWf0lJPXKrBV/pW3/aG1t4cEH7yI3178G9w9+8BNSUoJXGxHbPwRBEISzMmBz8bvXKv0CagC708uvXj7Cjz63AFURK2KCAGCcchP6cB+ug2tBO1XMTjVhnHE7rv2v+wfUAF4XhdVP8dDSr/LSlkacLl9TGVWRuGlmJnuPdQWUutR1eGbdUUpy4olv2BgQUAN4W6pw7XkZ09z7Lsr7vBx0XcNTux3XobfQh3uQIhMwTluJWjjvgqzKJyVZ+eMfn78AMw1OBNWCIAjXue2H23B7tKBj/cMuDtR2M1M0fBGEUaY592CYthJvc6Wvo2LmVF/g67IFP8DtYElsIws/v5AjdT14NI3JOQm0dQ+zdmdj0EN0HbYcamVV2/sh5+E+uumaCap1XcP+1i/wNh8Bj28Pum4fwLHpaZS63VhWfOGK3+4igmpBEITrXN9Q8L2eY+Ohk6wE4XolW2KQC+eN/r8Wpksj+FqGW0wqs0qSRx872hC+G2LfoAttOPR5dccguuZBkq/+cM5Tu90voB4bcOJtOoLn+A4MYZJKz0RXVycf+chDo/+/cuVqHnrokfM653hX/7+CIAiCMKp30Mnmgy109jtIibewuCzdr/SWrnnw1O31/fFSDKgFc8lMDt2+HCDTKrotCsJ4uqbhadiPt+Ggb6U6bxbKBF0Y5YRMPC1VeE7sBs2LkjWNjMSisMdkWiNRPJl4248FP2ds6jURUAO4Dr0VGFCP8DhxHVx33kG12P4hCIIgnJF9NZ3878tH8HjHtnK8trWeL9w1jSl5CeiOIWyv/witu3503H3kHaYWLiY+uoTewcA/aLmp0SJRURDG0d1O7G/+GG9bzehj7sr1KDkzkOLS0PtaA46RYlJwH9uGt2732DHVG4hLKWRqzhoO1wd2TIw0qywsS8NgXYX37eBBtaFs9QV4R1cGfbjnvMavBFf25hRBEAThjNgcbn7zaqVfQA3gdHv51cuHcbq9OLa/4BdQj5BqN/HYXI30JP8V6cKMWB69u+yizlsQrjbOPX/3C6hHeOv3YsidhZyU6/e4nJiNoXCeX0A9Qmuv5cNp1UzJ9f/imhRr5sv3VxBpNmDIm4Vp3oMwvsqHYsA44w6Mk2+8EG/piiBFJpzX+JVArFQLgiBcA3ZUdeB0e4OODTs87K1qYerxHSGPT2rZxHc/8Q1qGvvoHnCQlhhBbmrMxZquIFyVdF3HfXRTyHFP/V4i7/0PvO21aAMdyDHJKCmFDL/0LyGPMdZt5ssffpDmLhuNpzoqluTEI0tjHRWNZaswlCzB03QYdB01ozSg++PVzjhtJY5NTwffAqKaMJatuvSTOksiqBYEQbgG9E+QTNjXPwxed8hxzea7/VyUFXchpyUI1xave4KOin0AKCmFKCmFo4/r4ZIY3XbwOMlMjgqb3yAZLRjyZ5/tjK8aauE8lBO7A5MVVRNK5hTUgrnndf60tHT++tdXz3OW4YmgWhAE4RqQaZ0g2TA9EelkDLp9IOj4RElWgiCc6qgYm4Le3x50XEnIQhvuxX1006mVaiuG4iXIidl4mw4HP2e0FV3z4j60Dm93I3JEHIaiRchxwRuSXKskScay8gt4ju/AdXDdWJ3qslWoBXOv+HJ6IIJqQRCEa8L0oiSS4yx09NkDxjKtkUzNT8LVfxOu3X8LPFiSMExbeQlmKQhXP+O0VTg3PxN0TEkrYviFr4F3rEyla9+rGMtvCRlUq3kzsb34dXTn0NgxB17HtPBDGEuXXdjJX+EkScZQOP+8q3xcLld+2C8IgiBMSJFlvnx/Odmn3T7OT4/hS/eWI0kSxum3Ypi6EmRl7AmmSMw3fAI1rfgSz1gQrk7G0mUYZ9wOimHsQYMZ49z7cR162y+gBsDrwXVonS/Z0GAZe1xRMVTcgufkPr+AGgBdx7nlWbT+tov3RoQLTtJ1XZ/4aVe+7u4hNO2aeCuCIAjn5XhzP539dlLiI8hLC0w21Gx9eFuPIilGlMwpSKoxyFkEQQhHdwzhaakESUHNKMVTtxvHxqdCPt+0+CMYCufhaT7iq1OdPhmtpxn7a98PeYyx4lZMc+65GNO/6Nra6klJyUYal3B5NdF1nfb2BlJTc/wel2WJxMTg2+3E9g9BEIRrTEFGLAUZsSHH5Yg45PNM+hGE651kjsKQP2f0/zV7YK3p8XR7P5LBjCF35uhj3gmOGUkgvhqpqpHh4QEiI2OuusBa13WGhwdQz3LBQQTVgiAI1xGPV2NXdQeVJ3swqgqzSpKZnCOauwhXj+auYbYcamVg2EV2chQLy9KINI9txXC4PGw70s7x5n4izCoLp6aRkxrtdw5PSzWeEzvRPS7U9MmoBXOQxm3n0Gx9uKvfR+tvR45OxFC8GDnaGnZeSkJ22HE5IYvDdd3sOdqJ16szNT+BiuSM8OdMzMLbXou7dhu624GSWoShcB7SuJrVAzYXWw620tQ5THy0icVlaaQkRIQ975moru9lV3UHLo+X0pwEZpUkY1DHdg2PdG9t67FjjTOzqCyNpNix7S3x8VZ6ezsZGuo777lcDqpqJD4+/L/56cT2D0EQhOvEoM3Fj/60n6ZO//2b86ek8PFbS/3q4grClejt3Y288M4xxv+1j7IY+Mr9FeSkRtPea+NHf9pHz4B/iclbF+Rw15ICdF3HsfEpPDWb/cbl+Awst34N2RKDp+kw9rd+Dp5xe6NlBfPST2EIc4dH1zVsf/1ntN7mgDEpNpWn1QfZc6zb7/G8tBi+lLQBmgOTGCVTFEr+LDxVG/wfj0km4tavIUclUtvUz0//cgC70zM2LsGHV5ewpDw95FzD0XWdp16vYuth//3cmdZInnxwOtERRg6f6OYXfz+Eyz3WbEpVJD512xRmlSSf0+teLcJt/xCJioIgCNeJF9fXBgTUANuOtLPlUGBrZUG4kjR3DQcE1ABDdje/fuUIuq7zhzeqAwJqgNe21lNd34undltAQA2g9Tbj3Po8useF491f+QfUAJoXx4bfooUoSQmnSsKt+TKyNd/vcdmax77MhwICaoC61gFel1egZPl3LpViUzBOvzUgoAbQBzpwbHoaTdP535cP+wXUALoOz6w9Sld/YCWgM7H1cFtAQA3Q1DnMC+/W4nR7+fUrR/wCagCPV+d3r1UyZA9dD/9aJ7Z/CIIgXAecLi87qzpCjm8+2MrisnNb2RKES2HLwdaAgHpEW4+NndUd1DT2hTx+08FWsqUw3RDrduPOKA2sxDHC68FzfAfGqStCnkOOSiTyzn/B23kSbaAdOdqKkpzPO7/fGfKY96v6uPdLX4L+NrSeRiRLLEpaMY63/zvkMd6mQxyqbqB3MHjTJ03X2Xq4jdsX5oU8RyibDrSEHNtV3UFxdizDDk/QcZdHY0dlO8tnXp9170VQLQiCcB2wOT14vFrI8f5hV8gxQbgSTPQz2t5jCzs+MOxEV8Ik/mke9MGusOfQzzBxULHmolhzR/8/3NwdLi8ut4YlPh0lfuyLbdgkRV2nv28w7BzO9Zrut4VeafZ4Nbr6HRflda8FIqgWBEG4BjW0D7L1cBuDNhc5qTHMn5JCbKQx5B+80+tbC8KVJis5im1Hgo9JwLT8RF7bWh/yy2NWSjSylo3WG3wlVjJHI6cVwb7Qc5ASMvE0HcFzYhe6142aOQU1f7Z/kuNQty/JcaADOSYZQ8kSspOjOFzXE/Sc1jgzbo+XDfubaeoYIjbSxMKyNBITs9E6jgefiMFMdnYqEPruU3ZyFJ7Wo3iO70B3O1DTSlAL5/mV0OwfcvL+wVbauodJiDGzuCyNrOSokF9QYiONFGXFAfVhX/d6JYJqQRCEa8zr207y0sYTo/+/7Ug7b2w7yZzJKbyzpyng+ZIEK2eHr1wgCJfborI03theH3TPbsWkJPLSYlg0LZUN+wODZqNBZun0DIyulXhO7AQ9MPA2TL0JQ+ZUXNY8tM66gHEpOglP3W68dbtHH/Mc24J88E0ibvkakjkKT/0+7O/8Erxj2yNc+19n+ZRPcKSOoNtXZhYn883f7vDbUrFuZwMfXzSVafIm0AK3Whgm30huZhKTc+Kpqu8NGI+NMlLR8xb2nRvHzXUr8oE3sNz2deSIOKpO9vDzlw7hdHtHn7N2RwO3zM9BliS0IHUsbpqVyZTcBLJTomhoD9wmkxxvYXpRUpB3eX0QiYqCIAjXkJNtA34B9YgBm5uq+l5Wz8lGVcZ+9UdZDHzy1lIKM0PXtRaEK0GUxcCX7y/3KxcnAdMnJfHxW0oBePCmSSyYmupXySYhxsRjd5dhjbOgJOdjXvYZJPO4EnuyimHqSozTbwPAsuoxlNM6jMpJORiKl/gF1CO07kYc219Ed9mxr/+1X0Dte4KH/Ko/8MiKfCLNY2uZRlXm1gU57DnaEbBHWQee2tyPfd4nkSLixgYkBUPJjaMNYT77galMzUvwOzYjKZLHFijItRs5ndbfhnPLc7g9Gr9+5YhfQA3g1XRe31bPQysmEWUZW31XFZlVc7K4eV4OkiTx2D3lFJ32OyM3NZov31+BIl+/oaUoqScIgnANee6to6zfG1jSa8S/fGQWiTFmahr7MKgKk3Pi/WrPCsKVTtd1jjX1MzDsIis5KmhN5u5+B3WtA0SaVYqz45Fl/3KRuteDt6US3e1ESStGtgR2HvV2N6L1tyJHJaEk5zP8j38PvR1DMWKa/yDOzU+HnLdp8UegcDFV9b14vDolOXE0tg/xwz+F3m9y87wc7l6Si7elCt1lR0kpRI4MrCvf0jVMc5evTnVhRiy2N/4Tb1NgmT4AJIWj87/N/7xWG/J1711awE0zs6iq78Xt8TIpK46YiMBGKA3tg7T3+upU56YGfobXItFRURAE4ToxMEGS0MCwi9zUGGYWX9u1ZIVrlyRJp/b1hpYYayYx1hz6HIqKeloZu9MpiVkoiVmj/6+H637odaENB5bMG0+392MyKJQXjm2PGLBNfL1KsoKaOTXs89KTIklPijyzuepe+vtDVDgZ97oGVaasIDHs87JToslOiQ77nOuJCKqFy2LI7mbzwVYa2geJjjCycFqquDCFK4busuOu2YK3oxbJGImhaCFKsn/tWU9zJZ7jO9E9Tl9HttMSgLThXtzVG9H625CjkjCULEGOGQtkdc2D58RuPI2HQJIx5M1AyS5HksZWjb09jbiPbka396MkZqMWL0Yed9va7vSw9XDbaOe4BVPTyEqJZvfRzqDvS5YkUuItbDnUSuXJHgyqwuySZEpz48O2EdY0nX3HOtlf2wW6b//q9ElWv9U/ra8VV/X76MO9yPHpvvc77ra17nbiPrYVb1sNksGMWjgP9bRb7DWNfWyvbMfh8jApM475U1IwG8f+TPUPu9h0oMW3IhdlYnF5GmmJkQgXnq5peBr24Tm5D9BRsytQc2cgycroc9p6bGw60ELPoJP0xAgWl6cTFzXW6c/p9rL9SBtHG/swGxTmlqZQnO2/ynqsqY/tR9qxh/g31+wDvqS/3iakiDgMxYtR4sN3ITwTlSd7fN0C3V5KsuOZW5qC0TD23oJdv0piDp4Q1UGkyASUlELCVWiWE7LxNBzAU7cbXfOiZk0jM3FK2Hlmp0Sd03URl5iN1t0YfK6mKLKzUoDAWtSjr5scxZGTPeyq6sDt8TI5J4G5pckY1LHPqGfAwfsHWmjvtZMUa2ZxeTrJcWMdFT1ejd1HOzh0vAdFkZhZZGVaQaLf1pzGjiE2H2xl0OYiOyWaRWVpfttOdJft1O/i40imSAyT/H8X67pOZX1v2HleSmL7h3DJ1bcN8uMX9wckm9xzYwE3z8u5TLMSBB+trw3b6z9AH/ZP/jGW34xp7n3ouoZjw+/wHNvqNy7Hpfk6skXE4Wk8iP2tX4B33CqUpGBe+gkMhfPRXXZsr/8IrdN/77OSVYZl5ReRFBXXwbU4t7/gPzlTJBE3P4FizaOtx9c57vQ6tctmZLD9SDs2Z2By04wiK+09Npq7hv0en1uawidvC95R0eX28l9/OUB1Q5/f48VZcXzpvnJMBgVX1Qbfbe/xf04MZiyrH0dNK0Yb6sb26vfRB/2DfUPpMsyLHgHg2beO8t5p21aSYs189aHpJMVaqGns47/+cgCHa2wPqCTBh1YVc2PF+QdZwhjd48K+7r/wNlf6Pa6kFmFZ8xUkg4lNB1p4eu1Rv2Q2k1Hhi3eXMTknnp4BBz98fh8dff4NSJaUp/ORNSUA/N/bNbx7WuJsYozv39waZ8Hbdgzb2p+Aa/w5JEyLPoSxdNk5vTdN13nqtSq2HfEPKNMSI/jqg9OJjTKFvH6N02/Bte9V/5/zkfc+7wEM01Zhe+mf0XqCJAPHpSHHpOBt2O/3uJyUy+/ct3Ogri/gmOgIAxWTkth0wL8x05lcF59aFE1p1a9A855+Wowz7sA0607+37N7qG0OXNFOjDFTkB7Dzmr/yiIZSb6OijGRRg4e7+KXfz+M2zOW8KnIEp+8rZQ5k1OwOdz85wv7OdnmX/avojCJz905FVWReXN7PX/Z4L+dZnx3TG9fC/bXfohu8/9sjNNvwzT7bjRd53evVrK9sj3kPC8G0VFRuKL85tUjQbO3/7rhOA3t4etuCsLF5tj4VEBADeA68IZvdfrYtoCAGnwrtSMd2XzJSqfd1tW9ODY+hWYfwLn7bwEBNYC38SDuw2/h7WkKDKgBnMPY1/8KXdf5/RtVQRs/rN/bzJ2L80iOH1sxkiSYXZKMokgBATXAjsp2Nh8M3lHxtW31AQE1wNHGPl7fVo822IVz8zOBgYbbgePd/0XXvDg2PR0QUAO4K9fjPrmHfTWdAQE1QFe/g2fWHcWr+ZKqxgcO4HvJ59bV0NV3bp3jhOBcB94ICKgBvG01uPa9Qs+A79/l9OoQTpev057Hq/HcWzUBATXA+wda2F3dwf7aroCAGqB7wMEza6vRNQ37+l+dFlAD6Di3PIs2ELqUXDjbDrcFBNQArd02/vTusbDXr+vAG5gWfNA/yVExYKy4FcO0VUiShGX1l1FSi/wOlVMKMeTPDgioAbSukzyScoSKwiTGf6VNS4zglnk5AQE1nNl18dvNQ7jnfwLJMi6ZUFYxTF2BccYdAHzhrmmU5vrfOchOieKGivSAgBpOdbRcfwyny8tvXqn0C6jBl+T41OtVDNpcvPT+iYCAGmB/bRfr9zTR0D4YEFCD7y72b1711U10bPhdQEAN4Nr3Kp7Wo2w52BoQUI+f5+Ugtn8Il9Tx5n5au0MX6N98sJWHVohtIMLlofW34W0P/cvYfXRT0IB7hOfkHty1U8AZGLgC4PXgPrYVd5A2yWOvsTlsK2S9v52umkPUNoXeM3midYDvfWoex5r6R2+rRkcY+OLPQneT23yolSXlgR0Vw7Uv33ywhVuiDgUtTwag2/rw1G7H23go5Dnc1ZvYPBx61fHIiR52HGmfuHPcorPvHCcE5z4a+ufEfXQT27yz8Ya4Mzww7GJHZTsHjoduorL5UCsGJfSaXuXJXjqPHcIyFGKPsq7jrtmMadZdIc8RSrif5z1HOxksGkQKc/2ieYh8+Ce+xEGvGzW1GMk8tmopRyUQcfs38HY3oPW3I8ckoyTlMPzXfw75umrdFh798EN09Dlp6vTVqS7MjOW/XzoY8pgzuS4296Zy28M/xttS7UvITJ3kl5AZE2nkiQem09w1TGvXMImxZvLSYvjec3tCvu7u6g6Ks+KC3gkDcHs0tle2sy1Im/MRmw610jUQuoFMa7eNuuqjJHUELjyMvs7RTWxumh52no+sKvbbJnMpiKBauKQmTMqYYFwQLqZwwSyAbh9Ad4R5juZFH5wgWcnWF2T1bfwc+pEnmIe9P3gTiREDw66AZK7eQSceb+gtcqESHMMlPg7a3Oi28HPVBjsJXp3XR7cPhH0NHejsD78S3S9+b1xQuj30HUPdPkj/UPBAbkRnvyPYDolR/cMujGEqzujAQN8AlpDPYMKfu1AGwnQL9Go6QwODhFvW0e0DSIrhDJIcs1ESx2q/h00cdDvA4yIlIcKvksmFuC4kWZ0wyTEjKZKMcUmO4T4jj1enO0xADL7fNaevno83MOxiMMxrADj6Qy9ewKnfGxPM0+bwiKBauLZlWaOQpKBb0gDIEcmKwmWkxKWDYgBv8F/WSlIO2nBM6I5slhjk9BLY90ro10jOxxuXhtYXfMVMScrxJUQF2WJy6lWIz52EQT0WcPt1RHZKNN6uk74kR8cgSlIu0ZMWERtlpH8oREfFlGi0vjZc1RvRh3uQY1MxlNxAdko0da3BA5jslCiUpJywyVlKxhQ4uA7cwQMAJSmH7MhojrcEfw2zUWFKXgIvbz4Z8jXE740LS07KRmsPXm5NTswmJzUGCF22sTQnjnd2qSFXM3NSojCoCsdC3G0xGRXScnLQDoSfY1V9Lzsq23G6vRRnxTF/Siom4+nJhu+j9TYjRSX4OhumRNESZAsUQEyEgYTMdNzhXjcxB0/jwbGOihlTUAvm+iUpd/XZef9gCx29dqxxFpaUpxOZlIu3MfjKsxSTzKBLZtOekzSe6qi4uCyN7NTzvy6qTvawo6rD9xllxzG/NNhntBGtrxUpMgFjyQ3kpITpqBhlpDgrjtfCdFQsSI8hOd5CR2/waz4nJZrslCh2BNm6Ab7tatacfDikBm18A74vLTmu8POMjbo4e6rDEUG1cEklxVmYVZzMriD7tSLNKovK0i7DrATBRzJHYShejLtyfeCgasJQuhTdMYTn+E7QA1diDFNXYMgoxWXND7pnWopJRs2dge6y43z/D8FmgLFsNUpyAa79r6E7AlcM1dwZWJIzWDRtiPf2BQY2JoPCQmMltr/9bfQxz/EdSAfe4KbJH+elXYGr3LIksSylh+G//MRvK4fr4JusKPoUvwlxx3z13BzUwlikPX8Pui1GyShFTZ2EsXQprgNvBJ5AUTFMvYnl3jg2HWgN2l566fQMCjPiKM2Np/Jk4GvERRmZW5oSfILCOTGWrcHx9n8HHytfw5zcZP6+6QQ9A4Er1sVZcRRlxbNsZgavbQ0MvFRF4qaZWSiKxPsHWoJ+MbyxIp3I1GxsWdOCbh2SIuJ44UQiGw6P1XfeUdnOmzvq+dpDM0iIMeNprsS+7mfgGZuj+9A6lk79MLtkKej2lZtmZWHOyMUb5vp1H9+O9+Te0cc8tduRD67DctvXkM3R7K3p5FcvH/a7K7R2RwOPL55LVuMhgt216c++gf/32x3Yx30JeWd3I6vn+ho1net1UV3fyx/HBa47KttZu6OBrz00g/hoE56mw9jf+jl4xr5ouw+tY9nUD7O7OnhHxZWzspiSl0hOajT1QfZMpyREUDEpiUGbm2fWHQ38DIFVc7LJTonize0NQfOrZhYnk5SajKNoIe7qwAY2GMwYSpeyIlNld3VnyHlejiY0IlFRuOQ+enMJs0uSGV9oIDUhgi/fX0F0kOLygnApmeY/iKF4MUhjqzlSdBKW1Y8jR1tRrHmYl3/GPwFIUTGUrcZYcQtwqiNb+mS/88rWfCJufgJJVjGW3IBx9j1gGFdH1xSJaclHULPKkEyRWG5+Ajlu/B5nCTVvFuYbPwHAA8snsagsDWVcWbukWDOP3mQl+sjfOJ3uGOSG7r9y87wcv2YvMZFGPrkql4wjTwfujfa4mHr8jzy4LB+LaWwNxmJSeXD5JN91rJqw3PIk8rhb3XCqksnyzwFgnH03htLlMK4cmxSZgGXFoyjxGaQnRfLo3dNIiBkrx6bIEktnZHDXDb7yWZ+5Y2pAzdys5CieeGA6JsPlKZ91rTLkzcS08INgHNdUxWDxVbgonIdBVXjigelkp/hXQJian8Dn7vRtNfjAonxumpmJqoz9fMZHm/jcndPITI4iLTH4v/mN0zO4+4YCACzLPoOSXQHjUvjkhEyOT/4YGw4HbrPq7HPw3Fs16F4PjvW/8guoAdB1Ug8/w6dWZhM7rjKEqsisnpPNzfN91adCXb+GooV+AfUIrdeXWGx3evjta5UB26y8ms7Pt7qRFnwYyTTuM1NNGGZ+gJ8diPcLqMEXeq/d0cDDKyad03WxYnZW0CS+jl47//d2DbrXjWP9r/0Cat8La6QdeYZPrcrxq55hUGXWzMtm9Vzfdf7YPWWUZMf5HVqQHsNX7itHkWVunJ7BnYvz/K7NKIuBj9xcwpS8BKIjjHz5/nJSx3fHlGBWSTIfu9lXHca04GHUooUwrsyoFG31/S6OSqQgPZZP3zEl7DwvNVFST7hsuvrsNHQMERNhFC2ShSuONtyLt/MEkikKJXWSX/1o8NWZ9rYc9dWpTi3yS1Ya4e1tRutrQ45OQkkKLBepu+x4W4+CJKOkl/jdQh49R3stmr0fJSHLr871iN5B52jnuElZcbi2PBt8pf2UiDu/jT0qk2NNfRhVheLsOLTDa3Hu+HPIY8w3fQ4tcyZHG3vRdSjJjve7hTw6144TaMO9KPEZyHGpAeOard9Xb9ZgRkkrQTptJUnTdI429uFwesjPiPULfEa09dho7hwmIcZEXtr10cHtctE9Trwt1YDu+/cyBDZTqWsdoGfASXpSRNCa4QPDLo4392M2KhRlxwWsHmqaTk1jHzanh4L0GGLH1bkefU5/G96eZuTIeJTkfH765wMcOhE8d0GS4Id3RGN8/5ch35dx9t0oZbdwtLEPl8vXLXB8beQRp1+/w3//TtAVbAAUAwdmfIvfrwvRcRF4ZFUxN0yz+q55zYOSVkx1q5MfhemouGZeNncvKTjr6+LHL+zjSJAVbPDdmfrpHRZ4/1chX9c45z6Uaas52tCHy+NlUmbwz6i5c4i2Hl9HxWC9JmwODzWNfSiKREl2XND60bVN/QzYfN0xrXGBO+m1oR68XXUhfxd7vNqE87yQREdF4YqUFGchKcgFJAhXAjkyHjlyZshxXwJQ+MYNSnxG2EYVktGCmlMR/hwphYRbh42PNhEfbR39f/0Mki2jrAamTxo7xjHhMYOYjAplBUlhn6ck54edqxwRi5w7I/S4LDE5J7AF83ipCRF+q1vCxSOpJtTs8rDPyUuLIS/Mrr2YSCPTi6whx2VZomSCf3M5NhU5duxLWriEdl2HoYEhEsKcT7cPoioyU3LDPSvw+g2bpOx1YxsKXxJ2wOZCUo2oWdNGHxu0hT9mcNh9TtdFuCQ+TddxDfYS7r6w7hjwfUZ54T+jDGsUGdbgASZAhFmlYlL43xsTLarJUQnIUaHncSbzvFREUC0IwnVN17x4Tu7Fc+q2rppdjpo/C0ke+/XY3DXs1zluSXk6CTGhWyBfKPVtg2w+1Er/sIuclCgWl6VP2NBATsqBut3BByUZb0w6Ww+0nOqoKDO7JIWSxPBNl+SETNzHd+Kp962oqTnTUfNm+nXXa+4cYtPBVnoGnWQkRbKkPJ346MBVxwvNl5C5Cd0+gJyUg6F4iV/ZsGGHr3vr8ZYBoiwGFkxNpTBD3Bm7muWkRAXdzwu+3JzkrAw8+0MfLydm42k8hPv4TvA4UTJKMUyaj6SO/bx29zvYeKCFth4bSbFmlpSnEztBR8X09BQgdP3s7JQoPCf34a7bDac6KmYlha/MkZMajaelCk/tNnSXAyW1CEPRQiTj2IJU76CTTQdaaBrXUTE7JYrGjuCtyKMsBqIysnCFXiAPemdNmJjY/iEIwnVL97iwv/lj3+3YceSUQt/+Z4OZDfubeXbdUb+KNUaDzKN3l0240nU+gnUbizCpPH5/OQXpoYNCzT7A8J//KWitbD13Lj+onx5QK35OcSIfdDwXtEGLnFyAJCt422r8Hh/fXe+9vU2+vazjxk0GhS/eUzbhCtv5cO57Fdeul/wfNEUSseYrKMn5NHcO8aMX9geUJlszN5t7lxZetHkJF1dT5xDf+eOuoCUib1uQy51L8rG9+n28rdUB41JUIrK1AG/dTv/HY1OJuPVryJHxHKjt4n/+4d8tUJYkPr/ITGHlb4PWZTfNfxDD1JX86x92BQ1m05Mi+Hr6VvQm/wogcmI2T3k/wP4TfQHHxEQa+faU46i17wW8h4hbv44cY6W6vpefvXQQ5/iOisDN83NYu6MhaELmBxblcfuiPGyv/L+A6xp8+5Yj7/sekiLWXYMRHRUFQRCCcO1/PSCgBtDaa3HtfYXufgfPrasJKAHpcmv89lTnuIuhqWMoaLcxm9PDb1+tJNxaiGyJIWLNV/xulyPJqIXz+It9ftDmSzuPdnMo70PI1ny/x5XMqSjJBUH/8Pq6671KV5+d596uCahp4HR7+c1F/Iy8XfWBATX4uk6+95tTXSerg9b6fXNHA9X14evgCleuTGsUX7hrmt+dEFWRWTEriztONQGyrPgCymm1pOWkXAylywICagC9vw3ntudxur389tXAboGarvO/Wx1oCz6GNO5OCKoR44zbMZ7qqPile8sDEviKMmN5dFp/QEANoHU38CHrIWYWWf06KmZaI/nSImNAQA2gD3Xj2Pw0Hq/Gb1494hdQgy/J8Y3t9Ty8sijgM1o5O4tbF+YCYF75KMq4rSgAsjWPiFueFAH1ORKfmiAI163wnQ03sc07J2i5JvDtWTx4vJsZYfaLnqvNYbq+dfTaqWnsozg79AqwkpxP5P3fx9tei24fRE7KxmWMZUeYjorvHXMz/0P/grerHm2oGyUuDTkujaHnvhTyGHfNZra6ZoasO98/7OJwXQ8VheH3VJ6LcP92en8bTUerQtbXBl9nvYn28gpXrrKCJH702URqGvtwuLzkZ8QQM656lGSOImLNl31Jjr0tyFEJKEm52F79Xshzeur2UmVtDFlf2+PV2TGczYqHfuL7Mu51+xLnTGMJmvHRJr760AyaO4d8darjLWRaoxj+67cI9fVSrdvK5z7yQboGXKMdFfPTY7Cv+xnBZwLepiMcrmqgL0TdeV2H/iEXP/zsfGoa+3G6vBRmxvol8cnmaCLWfAWtrxVvXytyVKLY9nGeRFAtCMJ1K2znOMcQA8PhO8cNXqROfhN1G5tofISSMrbFwT5BR8WR96Ik5fj9YQ1WK3t0zD44YRfUi/UZhfu3A+jvnyABLEh9XOHqci5JjuF+ntG9OAbDJ+0O2t1IysRJyqcn8IVNIPY4wePCGmfxq34RvsOrzsBA8D3To3O1uVBkecItWPKpL9DC+RNBtSAI1y0lKQdv+7GgY3JSDrlp4TvHZVqj2H6kjQPHu5GAiklJzCy2+pUNa2gfZNPBVvqGnGRaowIS+GwOD1sOt3KssQ+LSWXelFRyUqPZdqQt6GtK+PZnbjrYwuETPaiKzKxiK+WTkpDHFX/3dpzAXbPZl8BnzSV60mLioowhV7ZyUqNp6Rrm/QMtdA84SDuVkGlJykHrCF5GTLbmkJMavpthjjUC97GteBoOgCSh5s5AzfVPcmxoH2TTgVb6hp1kJfs+o7io8Z+Rm82H2qht6iPCrDJ/Sip5STl4jm8P/qKSRFZeDur71SG3nwQr/yVc++TEnLAdUVOzMoDgyYgA2cmReOr34z6xy7dSnTkFQ+E8vyRHbaADd9UGtIEOpGgrxsk3IofrqBibgu5x4jr8Flp3I1JELIbiJShJuWgdIUr0GSzk5qbD+6ETI0Wn0UtPJCoKgnDdcp/ci+OtnwcdMy//LOTM4hu/2UH3gCNgvCgrFo9X58RpbYSLsuJ4/L5yTAaFt3Y18uK7x/z2G5uNCo/dU0ZxdjztvTZ++Pw+egf9V8QXTkvj0PGuoGWxKgoT6ex30Nzpn4hYXpDI5++ahqrIOHf/Hdfel/0PNEWyOeuT/GVnYEdFRZZYPTebN7bX+23lMKgyTy7USTn0dPDP6KbPo2fN4Bu/3R60u15pTiyfNb+G1lnn/3rpk7GsfhxJNbJ2RwN/fs+/JbbFpPDYPeUUZcXR1mPjh8/vDfgysKo8nls6fx+862T+HCw3fY5n1lazYX9gAGUxKfz7x+dekgouwpXF23US2z/+HbTAjqjGOfdgqriV/3hmd9D24CnxFr6ZsxO9wb9shhyfjuXWryNbYnCf2OVrPDP+/LKCofwW3PteJVhHRUPZGtxVG8BtD3z8yDvgDfw9YKy4BdOce/nxi/s5Uhd4TcdHm/jep+ZhFI2RLjiRqCgIghCEIXcGpkUfhnF7IjFGYFrwMIaCub7OcQ9WkJc2tuIj4QtgM5OjAgJqgJrGPt7cXk9bjy0goAZwuLz85tVKNE3nmbVHAwJq8O33vWVBDpnWsXlJEsyZnEyESQ0IqAEOHO/mvb3NeDtOBAbUAM5hFnf9hVsX5GI0jP3qj40y8sGVRazd0RCwN9rt0fjZDgV1/sP+n5EpEtPCD2LIn43R4OuuN37FWgIqCpP4WMaxgIAawNtShevgm7R2D/OX0wJqALvTy29fPYKm6/zxzeqgq+vrDvTSVPYJ5PF1wCUZtWAe5hs+DsCDNxWx+LSukynxFh6/t0IE1NcpJSkX802fQ4octyVCMWKsuAVjua8j6qN3lzH1tLrHhRmxPDrTHhBQA2i9LTi3v4DusuHY8LvAgF3z4j64FtOiR/yTHA1mjLPuwlO3KyCgBnAffBPTwg8iRY/LSVBUDFNXYJx1NwCfvWMK0ycl+SU55qRG88QDFSKgvgzESrUgCNc93ePyVbjQdZS0Ir9buSMa2gfpGXSSlhhBSnwEj/18U8i9zYkxJuZPTeW1rfUhX/MTt0zmd69XhRyfUWTlC3dNo651gP5hF5nWSOKiTHzhp+/j8oTY0pAcxdeLqsN3VPzAv+CMyaK2uR+DqlCUFctbuxr5y3uhO8F95o4pzJ4UP1oFREktCtr9ceQzSk+MIDk+gsGnPx+0tB/4ynatTfsMb2wP/Rl9/NbJPPVa6M9oVkkyn/vAVLwdJ9AdA8gJWchRiQHP6xtyUt82SKTFQEF6DJIkBTmbcD3RNa/v59njQkkp9Es2HNHWY/PVqY4xk5kcNUFHRRXjvAdxbXk25GuaFj2CoXiJ73U1D0rKJLydddhf/2HIY4zlN2Occw/etmPgtiNb8/3qsI/o6LPTcqpO9URbsoTzIzoqCoIghCGpRtTM8E0YslOiR/fh6rrOUJhkwUGbe8Jkwq4gW0rGGzqV4De+FfeQ3R0yoAZfEtWEHRUdg0QkG/y6Iw4Oh5/rkN199p+RpoUMqEfmMVGSY3f/mX1GSnJ+2OfFRZmIK7z4jWiEq4ckK6jpk8M+5/QuheE7KnrQbeHLNOqOQV+SY0ap32PhaPZBJElGTSsO+7zkOAvJokPxZSeCakEQhLMkSRLZqdEhu7rlpkZPuFo0LT+RtTsacLgC93YC5KTGUNc6wOaDvo6K2SlRLC5LIznOQkdf4K3ikdeVrblhOioqeKIz2Lyv2a+jYnZq6DbDMJaQub/Wl8BVUZjErJJkVGVsG0ljxxDvH2ihd9BJelIkN1akY0rMQutuDHpOxZpHXmo0mw8GLx8oAWUFiby5vQGnO/hnlJsauGInCBeLkpQbuqNiVCJKajFuXgt5vJyYg7tuN56RjoqZ05Ctefh+2oPfaVesuec/ceGSEUG1IAjCOVgzN5tfvXwk6NjquTlMzonn1S0ng+6ZrihMIi8thqXTM3hzR0PAuNEgI0nw70+PBcd7azpZt7ORG8rTWbsz8BhZklg1JxtDUi7uA2vRnYHltrTcOfzbX2pp7xlrALPtSDvTJyWFDNYLM2L564bj1Db3jz62s6qDd/c28ZX7KzAbVd7d08Tz4xrA7K3p5K1dDXxtwRKSuv8vyCckYSxbzbyUVF7ZepL+IHumKyYlkZsaww0V6by1KzAwNxkUls7ICHhcEC4WY9lqPCf3Bu2oaCxbjZo5FTkpB60rcEuTnJCJ6/DbaM1jvzM8J3YhJ2ShZJfhbTgQcIxkicUwacGFfRPCRSUSFQVBEM7BnMkpPLyiiEjz2NpElMXAh1cXUzEpCZNR4YkHKvwT+CSYWWzlk7f5bv/efUMBN83M9FvxtcaZeeimoqCBpN3pYU9NB3ctycNsHEtCio008snbSinKikO2xGC55YnTEvgU1KKF/Hl4jl9APWLfsS4WlaVRmDHW/nxkpTgvLdovoB5xvHmA17fV09Fn5/l3AjsqutwaP9ttwTD3QTCO3UKXzNGYl3wUNbsci0nlyQemk50ytlIuSb690p+41fcZ3bu0gGUzMlCVsX3QyXEWvnRvmV9NX0G42JSUQszLP4sUETf2oMGMceadGKeuQJIkLKsfRxm3vQN81W7U3Bl+AfUIracRyRyNmj/H98N/ipyYjeWWryIZxc/41UQkKgqCIJwHl9vLsaZ+JAkmZcZiUAMz7hvaB+kbcpKeGElSkEBwwObiZOsgESaVgowYXlxfGzSoHvG1h6aTnRLN8eZ+FEVmUmasX2A+wpfAN4ScmIVTjeaLP9uEN8TvyYKMGL75oVk0dQ7RM+AgJcGXkPn4LzYHXUkGX+WQGysyeHlzYIWPEV+8u4zyvGhfopUk+zrQKYaA59W3DdI/7CQjKYrE2MDKHAPDLk62jX1GItlQuFxGkxy9Hl+SY5DAV+tr9dWpjrGixKUz/JdvofU2BT+haiLqI79Et/Wj9TQhRcSiJOVe3DchnDORqCgIgnCRGA0KU04rv3W68Ql8wcREGCkrGKtacSYdFS0mlan5gZUuxhufwOcYdIYMqMe/ZqY1isxxneDCJWQO2dwTdkwctLmQVNOESY6+Ff0wn1Gk/2ckCJfLmSQ5nt6lMGySo8cJHjdyVGLQ6jXC1UME1YIgCFeY3LQwHRUlSEuKYOP+Zl9HRVVmZpGVGUVW5HH1mGub+tl8qJVBm4vc1GgWlfk6OQbb4w2+JEdvTyPuqo3oQ93IcWkYJt9Iblo0x5uDBwS5qdGnqpOE7jqZmyaSCQVBtuYF3TcNIMWmim0e1wgRVAuCIFxhFk5N4/Vt9QwMB0ngK0zif/9xhJausXJ1OyrbmZqfwBfvLkNVZF7aeJzXt40lS+071sVbuxpZMDWVt3cH3oJWZIll1k5sL/2Y0Q4w9ftwHX6LlSWf4n9DxMyr5+ZQVpDAPzbVBe06OTU/gazk8JVFBOF6YCxbg73xIAEdlgBj+ZrLMCPhYhCJioIgCFeYCLPKkw9UkD0uIJUliXlTUjAbFb+AesThEz2s39vM8eZ+v4B6xLDDw+G6Hj6wKA/TuCTH+GgTn1mTS9qR5wL/4Hs9lNQ+zSMrComyjO2DjrIYeGRVMTOLraNdJ/PTx1akJQlmFln5zO3ht3wIwvVCTS/BvPTTSJaxZGCMFkxz78NYcsPlm5hwQYlERUEQhCtYfZuvSUpGUiTREUYe/a/QHRWzkqMoyIhlw77Q2zG+9cgs0hIjON7Sj1FVKMyIxX3wTVw7/xzyGPPyz0LOLGqbfFVACkMkZDZ1DNE75CQtISJoQqYgXO90zYO3rRY0ry/J0SCaEl1tRKKiIAjCVWp8Sb6JOioO2d2jXQZDP8eFxRTD1LyxhKiwSVT4ur4ZVYXJueETMjOTo8gU2z0EISRJVlHTSy73NISLRATVgiAIV4lIs0pyvIWO3tAdFfPSYth9tDPouCJLJMdZeHdP07iOislMSwrf5ltPyGXLoVZfR0Xd15hlzuQUDKrYQShcW/qHnGw80MLJ1kEiLSqLpqVRnB0/Oq5rGp6Te/Cc2AVeN0rmFAxFi5AMY2UgW7uH2bCvhY5eG9Z4CzdUZJCRFHle89I1D57jO081n9FRssswFM5HUo2jz/F2N+Cu2oA21IMcl4px8lLk2JTzel3h7IjtH4IgCFeRjfubeXrt0YDHZUniaw9PJzUhgm/+dgdD9sBSeLNLkqlvHwwIyqcXxPNR7QX0/vaAY6TUYv6rbwUnWvxXs/PSYnjigQosJrE2I1wbTrYN8OMX9jPs8Pg9vmpOFvcvm4SuebCv+znexoN+41JsKhG3fR05Io4dle387rVKv/KVsiTxsVtKWDA1jXOhe5zY3/ixrzb2OHJiNhG3fg3JFImrcj3Ozc/i1+5cUbEs/zxq7vRzel0huHDbP8QygyAIwlXkhooM7r2xwC+YjY828Zk7pjApM47oCCNPPFDhV2takSUWl6WhaXrQVe59x3s5kP0hlLTicY9KqDnTeSfy1oCAGqCudYDXtp68kG9NEC6rp16vCgioAdbtbORoQy/uyg0BATWA3t+Gc/sL2Bxu/vBmVUA9eE3XeXrt0aBfdM+E68CbAQE1gNbdgHP339CGenBueQ5O72vq9WDf8Ft0T/AymsKFJ5YYBEEQrjJr5uWwbEYmx1v6URWZgowYFHlsjSQ7JZrvfHwO9W2DDNndZFojMRoUvvizTSHPueGYg4WP/BPevhb0oR7k2BTkaCsb/3tzyGO2Hm7j3qWFF/S9CcLlUN82SHNnYFWdEVsPt5Hp3BJy3HNiN/tiV+FyB895cHs0dlW1s3RG5lnPzV0T+nXdNVuRIhNAD5Fr4bLhqd+PoWDuWb+ucPZEUC0IgnAVMhkVSidIHByf5Ng7QUfFkVU0JS4d4tIDHg93jCBc7Sb6WR6yu9FdQ6GfoHlw2EIH5QCD53i96M4wr+u2o9snSjQOc7xwQYmgWhAE4Rp0tKH3VEdFN7mp0SwuSychxkTPQPBbwXlpMdS3DbLxQAs9Aw5SEyJYOj2DvPSY0VJ6wY4RhGtBVnIUqiLh8Qb/4pmXFoNiy8MzGDwJWIq2kpWZCnSEfI2clGi2V7ax52gnXq/OtPwEFkxN86sb39QxxIb9zXT1O0iOt3BjRQZx1ny8zUeCnlNOzEZJKcR9aF3I11WseSHHhAtLBNWCIAjXmL+8V8ubOxpG///g8W7e3t3E4rI03trVGPB8RZaIjzHxnT/uGt2VefB4N+v3NrFqTnbIoHrNvOyLMX1BuORiIo0smpbGhv0tAWNRFgNLytMxDq/GU7cHdG/Ac4zla5icl0ReWjR1rYMB41nJUazb2UB1Q9/oY/tru3h3bzNffWg6MRFGNh1o4Y9rq/16ML23t5kvLZpHdnMlAXumAWPFLai5M5BjU9H62wLGlfTJKMnhq/sIF45IVBQEQbiG1Db1+wXUI+xODwePd3PXknwsprGVscQYMx9eXcy6HY0Bf7I9Xp339jbzoZVFxESMdVSMiTDwkTUlTJ9kvVhvQxAuuYdWFHHj9AxURRp9LNMaxVfuryAm0oiSnI95xeeRosZqvGO0YJxzD8bSZQA8dm85ZQWJjJxBAqbmJVBRkOgXUI9o6RrmrxuO0z/s4tm3jgY2NdV0frFNQ178cb9ujJIpCtPCD2EomIskK1hueRIlbVz9a0lCzZ2JZcUXzvNTEc6GKKknCIJwDXlmbXXQ1bYR33xkJhlJkdS1DGAwKOSnx/Dm9npe2ngi5DGfvn0KM4utHG/2rVgXZMSiKmJNRrg2DdhcNHYMEW0xkJ0SHTCuaxpax3F0zYNizQ/aFbGjz05nnx1rrJnk+Ai+9bsdtHQF33NtVGXuXJLPi+trQ87pozeXsGhqMt7247461cn5fjWqR2h9bWhD3acSjZPO4l0LZ0p0VBQEQbhOTJRwNWx3Yzaqft0Rh+2BZcT8jnG4URXZrwmGIFyrYiKMTAmTBCzJMkrqpLDnSI6zkBxnGf3/cNely6MxMBy+E+qw3ePrxuhX9jKQHJeKHJca9jnCxSOCakEQhLOkaTp7azrZWd2By+2lJDueJeVpRJjHtki0dg/z3r5m2nvsJMaauaE83a8ah9PlZevhVg6d6EGSYEaRlbmlKX4rwDWNfWw62MLAsJvslChuqEgnKdZCOPnpsRN0VIzgrV2NVJ7swajKzCpJJi8tcDVuvKzkKN4/0MKB2i70Ux0V509JwaCObSPxdpzAXb0BbbgXOT4DY+ky5Jjk0fEhu5uN+5s51tSP2agwtzSFisIkJMl3o1zXdQ7UdrO9sg2Hy8ukzFiWlKcTHTG2GqcNdOCqXI/W24wcEY9h8g0oyQVh5y5cP3SXHXfNFjxNh3wBaP4s1PzZSPJYqONpqcJ9dDO6YxAlKQfD5KXIUWMBdO+gk437m6lrHSTKorJwWtqEVXbOREF6DPuOdQUdS0uMoDg7Lui2rRF5qRG4azbjObkXXdNQs8sxFC1AUsdWyb2dJ30dFYd7kGNTMZYuRY4bazijO4dxV7+Pp7UaSTWiFsxFzZ2BJIm7TheK2P4hCIJwFryaxi//dtjXsnucxBgzX3t4OkmxFnZXd/DrV474lbCTgA+uKmbp9AwGbS5++Pw+mk+7HVyUGcvj91dgMij8/f0TvHpacxWTUeFL95SFXTEesrv51m+3M2ALXBmbOzmZE60DdPY5/B6flp9AZ5+Dth5bwDHFWXE43V5OtvknX+WkRvPkA9OJMKu4DryJc8eL/gcqBiwrvoCaXU5bj40fPL+X/iH/1bi5pSl88rZSAH73WiXbj/h3dIyNNPLVh6aTlhiJp+Eg9rf/G7z+78s09z6M5TeH/DyE64Nm68P26vfRT0vWUzJKsax+HEkx4Nj+Au6Da/0PNFiIuPkrKCmF1LX6OiranP53bm6alclDNxWd1/xqGvv44fP70IKEXB+/ZTLzp6byb3/YRWNHYPm7wvRoHotdh9buvz1ETsjydVQ0R+E6/A7Orf+HXzKjrGJe/lkMeTPRBjqxvfo99OEev3OouTMx3/R5JFkE1mdKdFQUBEG4QDYfbA0IqAG6Bxz86Z1j2J0efv9GYFc1HXj+7Rp6B538/f0TAQE1QE1TP+t2NlDfNhgQUINvdft3r1UF/cM8Ispi4IkHpvutiquKzI0V6bg9WkBADXDoRA/zp6QwOWcsWJckmFVsJTM5KiCgBl+zjNe2nkTrb8O548+BE/G6cWz4HbrXzbPrjgYE1AA7KtvZVdXB7uqOgIAa8CVvrTuK7vXg2Pi7gIAawLnjL2h9gVUPhOuLc8dfAgJqAG9zJe7D7+BpOxYYUAO47dg3/A5d13nq9aqAgBrgnd1NVNf3ntf8irLi+MwdU0iIGVtZjrIYeHD5JBZOS0OWJL58XzlT8xPGkhwlmD4piU8XNgcE1ABaT+OpjordOLc9T0B1EM133egeJ46tzwUE1ACek3vwHAvdXEY4O2L7hyAIwlnYejh0AHegtpvtR3zbF4Lxajrbj7SxLUgAOWLb4baw+y+7BxwcbejzC4BPl5kcxbc/MpumziEGbW4yrJEYFDlsR8UDx7v51iOzaO+10dPvICUhgoQYM18K21GxlTtiughW6gtAdwzSefQAVfWhm1NsPdyGJIUcprqhj76aPaghG1zouGu3Ypp1V+iTCNc03evGc2JnyHH3sa0o/aGvOb2/jeaqgyETCcH3c1oS5po7E7NKkplRZKWudQCPVyM/PcZvC1VslIkv31dBR5+d7j47yfERJMaaGfrTsyGuMN97k6LCdVS0467ZGrS9+ug5arZgKF58Hu9MGCGCakEQhLMw7Aid1KfpOn1BVmTHG7S5cLqDB90j558wcfAMO7NlWsduUU7UUXHknCnxEaTER5zRaw07PBN2a7MNBW4p8T+HmzAxNQCuoYGwf6x0R/hOdsI1zu0MehdjhO4cDt+VEHAOBd6NGW/YcWG6h8qyREFGbNjnnJ7kGPYaczvQ7eHnrtv6CKjVN37cKa6fC0UE1YIgCGchPy0m5IpWfLSJqXkJQbdujJiUFcfhul6aOoP/ocxPjyE/PYZtR4KviEsS5CYZcR1+G0/jISRFRc2bhVowxz8hq7kS99FNpxKycomefCOJMSa6Q3RUzE+Poa51gI37m+kZcPo6Ks7IID89hmMhmr/kp8egpBTgrnw35PtNzc0j0lwX8stIfnoMEhLHW4KvREeaVWJzJ+HeF/IlrpjmFv3DLl8yZmMfTrcXHYkIk0JJdjyLy9OJshgmPolw1iRzFFJsatDtH+D7+VCSC/DU7Q5+AlnBWlCMuvFwyI6K+ekxHD7RzdZTd5Ly02O4oSKD+Oix7Rzd/Q427G+mvm2Q6Agji8rS/O4o6V4PnuM78NTtRte8qFnTMBQtQjKOBdD1bYNs3N9MZ7+DlHgLN07PICGlAG/T4eBTT8qZuKNiVhlS1Xsh25mLZN8LRwTVgiAIZ2Hl7Cy2V7bj8Qbebl01J5tJWXGUZMcFbfSQYY2kvCAJl1vj168Eth2WJYnVc7LJSY3mje319A4GBsBzixIwv/s9nONuZ3tO7kU5usmXkKUacWz7k98fWW/TYTjyLqsmf5LndwSeU1UkYiONfPfp3aO3mQ/X9fDevmZWz/V1VDw91JCANfNyUPNikfb8A30gsD2zkl2BOTmLFbM9/GNTXcC4yaCwfGYmEvD+wRacQbbN3DQrC7M1Cz1nOp76wMhairaiFswJePxSa+oY4od/2hd0686hEz28u7eJrz00A2tc+OotwrkxVdyCY+NTgQOSgrFsNXJsKq6DbwYNLA1FizAnWllcns57e5sDxqMsBtq6bX613A/X9fDunia+8kAFuakx1DT28dO/HPD7Gd52pI01c7O5d2khuseF/c0f4209OjrubTyI+8i7WG77J+SIWDbub+aZdWMNYI7Uwcb9LXxx4Txym48EXW0e7agYl47WF1ifXsmYgppSgLFsTWAyMYBixDBtReDjwjkRiYqCIAhnITM5isfuKSMlfiw4sphU7lqSz8rZWQB84a5pzCy2ju4VHumq9pX7K5BlibmlKXxoZRHR47oUJsSY+PQdUyjJicdiUnnywekUZMSMjquKxJLyNB6I2YUeZH+ot6UK95F38bTVBF+1ctuZ1/Fn7rkxn0jz2HpKcpyFD68u4a1dTQGBs1fTWb+3mUdWFxMbNVbaLjbKyMdumewriacYiLjlq6d1c1NQC+djWf4ZAG5bkMutC3IxG8f2j6YnRfL4feWkxEeQHB/B4/eWk54UOTpuMircuiCH2xfmAmBe9mnUwvkgjZ1DSSsh4tavIimXfwX46bXVYffC9ww4+dM7xy7hjK4vhuLFmOY/hGQa2/IkRSdhXvF5lJRCJHMUllu+hmzNGztIUTGULsO08EMAPLh8EstnZmJQx0KjnNRo7lqSz5YguRTDDg+/f70aTdd56vXKoF8K39zRwPGWflyH3/ILqEdo/W04d/6V/iEnz71VE7Sj4i+3e5AWfRIpcqy0n2SJwbT4Ixjy54x1VMyYMnagJKPmz8Gy4vOAr426cdadMG5VXIpNxbL6SyjxGSE+VeFsiZJ6giAI50DXderbB3G6vOSmxWAyKAHP6Rlw0NHrq1MdbIXS49Woax1AliTy0mKQ5cDdxa3dwwwMu0hLiiTaJDH0x8+CN/hWCjkhEyW5EHf1hpDzjrjjW3gT8jjZNohBlck9tSoerqPip24vZXZJMnWtg6BDXno0SpASXFp/u69GblwackRcwLjd6aGhfRCzUfWrTjJeQ/sgdqeH7JRoLKbAm6marQ+trxU5Mh459spoctHeY+OffrN9wudJEvzsi4vFNpCLSPe48HadRJJVZGtu0BrM3t5mdPsgSkImkjmwNNqQ3U1z5xBRFgMZ1ih+++qRsMnFH725hD+8UR1yfOmMDO7qezroSjIAipFt077JC+uPh36NNSUsmpaK1nkCXddRrHlISpDrY6DjVEfFVOTIwMRK3e30fT6qCTkpZ7ROvHDmREdFQRCEC0ySJHJTY8I+JyHGTEKMOeS4qshMyowLe460xEjSEn0ruLpzOGRA7Ru3TZiQpTuHMRoUirLGXjdc8iWAzeFBkWUKJ0iwkmNTkGNTQo5bTOqEXRmDtYX2e42IuKAB++U00ec3QtfB5vSIoPoiklQjamr4mtJKfAaE+TGMshj8fk4n+vftGwqepzB6vN0dPhnQ62LYNsE5HB5fJ8eUwrDPk2OS/ZounU4ymCbsyiicOxFUC4JwxdA1Dc+JnXhO7EL3OFHSJ2MsucFvNam5c+hUp0Kbr1NhRQZ5aWPBre524D66CU/jIZAkDLkzUSfN99siUFXfy+aDLQwMu8hKiWbp9Ay/leSRrmrHm/uJMBtYMDWV8sKk0XFN09lV3cGukY6KOfEsOS0RzdvTjLtyPVp/G3J0IoaSG/0S6s51nhGxqWjhErJSwidkSTHJuA68iaf5iC8AyZ9Nflpe8OefkpdswVX5Hp6G/aDrqDnTA7q51Tb1s/FAM72DTtKTIlk2I5PUhLEqIgOnkvhqTnVUnFeawvQiK/KplTJN19lX0+XXUfHGigxiIse2nbT12HhvbzPNXUPER5lYUpHu96VE9zhx12wNO09v2zFc1RvRh3uR49Mxli73a+s80Tx1XcNzch+e2m3obgdK6iRSC5ZgNiohSymOiIsykjiuTrEwRtc8eI6fuva9btTMKRiKlyCZxrYEebsbcFe+hzbQgRxjxTB5KUpSztg5XHbcR9/H03TY11ExbxZqwVy/Fd0jdT1sOdTKoM1FTmoMS6dnkBg79sVXG+rGXfke3s46JHMUhqJFFKTHcPB4d9B5G1WZGZOsvLL5ZMjqOgXpsSiWAjwn9wYdl+PTKchKhO1NIT+f/BQLruqNvnPoOmp2mS/J0TA29xMtA2zY30zvgIPUhEhunJFBxrgtVSNdTY829GE0KMyZnMys4uTRO2QjXU23HmnD7nBTkBHLjdMziIsa+5kd7Wra3YgUEYuheAlq+ritX9e5y7L94+WXX+Y3v/kNAEuWLOFrX/saVVVVfOtb32JoaIhZs2bxb//2b6jqmcf8YvuHIFzddM2Dfd3PA+qpSpEJRNz2T8gxVnZUtvO71yoDOhU+tKKI5TMz0ewD2F/9Hlpfq9855JRCIm5+Eslg4qWNx3l9W73fuNEg89jdZUzOTaC+bZD/fGFfwOrUorI0PnbzZDxejV/87VDAH9n4aBNff9iXiOau3Y7jvd+CPj7IkjAteBjj1JvOa57/NM9BwpEXAj9ASSHijm8gx6Qw/JdvBE3IUgvn4W07hj50WoCQMZUftCyipTuw/N2UnFg+Y3wFrdt/LnJitq+bmymS17edDNg+oioyn/vAVComJdHSNcwP/7SPgWH/coOzSpL5zB2+faC/eeUIO6v8kx1jIgw8+dAMMpIi2V/bxf/8/XBAgujdN+Rzy/xcdOcwttd+GHaezv2v4dr5V/83qKhYbvo8as70CecpoeN491cBNZElSwxrkz/GG/sCm2uM98DySaP77oUxuteNfe1/4W32T96Voq2+az8qAXfNZl8i4viQRZIwLf4IxpIb0IZ7fR0DT0uYVdJKsKz5MpJq5Pl3anhnt3/gajYqfOnecoqy4vC212J748fgtvs9x164gu8eyg66Z37FrCwevGkSv3+jis0HWwPG46KMfPcT8zD11WF79fun/U44NYeln0IpnM93/riLhvbAO02TMmL4YvQbaB3+15gcn4Hltq8jm6N5a1cjL7zrv2dfkSU+ffsUZpUk09Fr4wfP7wtIfq4oTOLzd01FliR+/3pVwN5xXzOpCrJTovE0Hca+7ufg9b8+jDNuv67qxF9RHRXtdjv/8R//wbPPPsvLL7/M7t272bp1K08++ST//M//zLp169B1nT//OUiHLkEQrlnu6k1BGxTowz04tz2P3enhj2urg3Yq/NM7x+gZcODa9VJAoAqgtdfiOriWutaBgEAVwOXW+P0bVWiazh/frA56u3fzwVYO1Hax+WBr0FWr3kFfIprusuPY9Mcgfzx1nNueRxvqOa95/uJANMb5DyOZx7ZJSNFWLCu/gJJc4EvIuvVryOPLzCkGDKXL0d3OwIAaoPkwX5w+xNS8sW5usiQxZ3IyH8uqDQhUAbTuBpz7XqWtx8bfguzH9nh9n6nbo/HcW0cDAlWA3dUd7KzydVU8PaAGGLC5eW7dUdwejd+/XhW04spLG0/Q2j2Mc9+rYeep9bXh2vlS4Hv3enBseArd655wnr6V1MAmI7p9gNXON7l5Xg4mY+De+giTyt035IuAOgR35fqAgBpAH+zEuf0FdMcQjk1PB1a/0HWcm59Fsw/g3PHnoBVovK3VuI+8Q01jX0BADeBwefnDG1Xouo5j4+8DAmoAS+3bPL4sxi8PwGiQWTk7i/uW+crRfWhlMUunZ6AqY2FVQUYMTz44nQizipI6CfOKzyFFj93xkszRmBZ+EMOkBac6KlZQXpDodw3OLLby6cLGgIAaQOttxrX773T123lxfWASrFfT+cObVThdXv7v7WNBqwntr+1i66E29h/rCpqMOWR38/Tao+iaF8eG3wUE1ACuva/g7Qq89q5Hl3z7h9frRdM07HY7EREReDweVFXF4XBQUVEBwF133cXPf/5zHnrooUs9PUEQLhPPsa2hxxr2c+RIfdDsevBtHdhR2cai2tDJYu5jW9nWMznkePeAky2HW6lvD91IYduRNnqC/GEaceB4F/01AyjuwFbgAOga7tptuM9znieiZ1Ly8I1onSdBUU8lHI39MVfiM4j8wL/g7WvxJWTFZ4CsMPT0F0KeN7JxK1++/1/o6rPTPeDrqBgXZWLo2d+E7ObmqdnCNteckONDdjfbDrcFLS84YuvhttGtFcEcbewbrQ0cyrYj7aysD/PzU7MFSTUSsvOjc4jOo/upbgj9b7/1cBtl0aFfQ287yl3L4rhlfg6NHUNEmFW8Xh2n20tOanTQRFbBxx3u2j+5B1dKYejmLpoHd+02PHW7wp5/a2ToWubtvXaOHakmLVQiIZDavYtvf+QTNHcNM2x3k2mNJMI8tlXLoMp8aFUxdy7Jp6VrmOgIw2guxOhzcmei5kxH66oHrwf5tGTDmEgjj91bTne/g65+X0fF+GgTQ396OmxHxR3KkpC9XexOL1uPtHL4RPDtK+D72Y6KCL3Pv651gK7qfZhtfSGf4z621W8rzvXqkgfVUVFRPPbYY6xZswaz2cycOXMwGAxYrdbR51itVtrbQ2faBhNqKV4QhKtDo9dOyB2puo4qhe9opkuAJ3TAK7ltoc9/ijtEp9/Rca+OM8yTdB0kwic1WWQXrvOcp2IykJyaAKkJ4Z9oHUtI8gx0MxTk1vPoOb12rNZorFb/RMFBZ+iOiLrLhj5B9QD3BDsM3R4dSQr/HM8E59AlCX2CeZplN2F7XYb5bMA3T1Vzhv23iYsAU0o82Znn1876emP32Al5VWleImRn2H87i+TEFSaBV3LbCdHTZcwE//5GnEGvj9NZgbzsCa7L5LLw5zjtNYZdtpBBNW5H6M/uFI8uhT4ecHo0TBN8QGr4qweT5J7ws7keXPKgurq6mpdeeon33nuP6OhonnjiCbZs2RLwvLMt8yL2VAvCVS4xH7qCJ+pIkQkkJKcCoVeS0hOikBOz0bobgp/Dmk9mYkTQMfDdai3J9JXGC9VGPMsaSZRZpTHEanZ8tImIpFRCrFMD4IrNPe95RqoSz71+hMMnulEVmZnFycybkuJ369nTeAh3zWZ0+wCKNQ918g1IUYnBt38AemIe2/c3sWGko2JiBMumZxCfUhC0vi74OrGlJ4RvZlKYFkOUxRBypTk7OQpJgur63qDjkWaVovTwVVbS4y0oE8zTFZMd9hwxyZlEWerCzlOLzIXm4K+BKZJ+LRqpM3zLaCGIpHzoC9y6ASDFJOOIC1/xwhVXgByXFnRLFYCUlEdmbOhrSlUkrOnpoKghq+t44nJp3bMJd80WdMcQSnIBhtKlyFGJo8/RBjp9SXxdJ5EsMRiKF6NmTh0d171uPLXbcdftBs2LmjkNQ8liJOPY3Lyddb5kzMFO5NgUDKXLkKz5ELKjYi7pCZFBx0YUpkYTG2Wkfyh4YJybEkVUhIGDtV1Bx81GBXNKPh5JCtnu3B2bTed18rN/Re2p3rx5M/PnzycxMRGj0chdd93Fjh076Ooa+8fs7OwkOTl0SRhBEK49hmmrIEQTD2P5GvIz4vxa/o6XaY2irDARY8WtwU8uyRjLb2belFQSQlRfmD8lhfSkKJbNCN4IIdKscuP0DFbOzvJrDjHemrnZGFILUDJKg47LCVko2eXnNc8ZxVZ+/tJBXlxfy5GTvRw43s3v36jiP1/Yj+vUlwHHluewv/ljPMd34G2pwnXgDWwvfRs1b1bw11VUdssV/Meze9hyqI2q+l7e29vMv/5hF3UJC4BgixwSxopbmFWS7NcIZ7zpk5LISY0OuZfYbFRYPiuTZTMz/RrDjLdydhZZKdHMKLIGHU+OtzCrJBlj+S1h56nmzUYKUe5PzZ2B2Zo54TyNU5aDIXiJROO0Vae2mAhny1i2GuTga3zGilswZExGDlFKTrbmo2SUYqy4JfjJZQVj+c0sKksjNjL4v8+isnTiExMwlNwYdFyyxKD1NGNf+194TuzyXVP7X2P4r9/Ce2qvs6eliuG/fgv3wTfxtlThOb4D+xv/iWPbnwBfZRr7az/EsfEpvA0H8DYdxrn9Twz/7d/QTm2rcFWux/b37+A++r6vmVPVBmx/+1fkxBwIsdBonH4r0ycl+VX5GG9qXgJ56TGsmRP8S6XRILNidhZLp2cSEaQuPMDymZlYElNRC+YF/3yiEjFMmh907HpzyYPqkpIStm7dis1mQ9d11q9fz5w5czCZTOzZsweAf/zjHyxZsuRST00QhMtIScjAsvpx5Li0sQdNkRjn3Idxqq+N7ufvnMacyckop0pASUBZQSJfub8cWZIwFMzBtPgjSJaxespSdBLmmz6HmlY82qmwKHNsXFVkbpyewSOrfWWh7r6hgDXzsv0SznJSo3nigenERZnIsEbxpXvKSBu3mhxpVrn3xgJumuULyiwrvoBaMHdc9z8JJbscy81fQZLk85qnIkFnX+BaeE1jH+/uacLTehT3kXcCP2C3A0/9foxzH/DvOhebimvR5/jj9sCqA15N5392grLkE/7d3CITMC/9JGp2Oaoi8+SD05mSO/aFR5ElFk5N5VO3+Sp73DI/hzsW5fk1c8m0RvLl+ypIjrOQHGfhy/dXkGkdm5fFpHD7Ql8nRoBP3lrKwmmpo//2AKW58Tz5wHQMqoyaXYZ52adCzlNSVF/nx/Fd52QFtWgh5qWfPqN5yjHJRNz8BHLCuODbYME44w6M028L/MyFM6Ik5WJZ9UW/Lz2SORrT/IcwltwAQMSqL/m+FI7kDkgSau4MLGseR5IkDEWLMC38oH8Cb0wylhWPoiTnE2k2BHQpNaoyy2dm8tBNkwAwzX/Q9+V+3JcjOTkf46w78dQG2fftsuPY+Ht0XfMlOQbZ1uU+tA5vey2uQ2/jbQ9MJtQH2nHu/AuarQ/n1v8jYN+/ruE+8jbmJR9DGrcqLkXEYV7yMQx5s5Blia88UMG0/LEkR0WWmFeawmc/4FspXzknm7tv8O+mmp4UyZfuKSctMZL4aBNPPFhBdsrYNWgyKtwyP4c7l/j2o5uXfBRDyQ2+Ff2R10kr9lXXUUWpSLhMJfV+85vf8Le//Q2DwcC0adP49re/TV1dHd/61rcYHh6mtLSU733vexiNZ/6tX2z/EIRrh7e7AdxOXwJekNW/viGnr1NhjNmvxuwIXfP4koEkOSCJb0R7r83XqTAxMmgzDrvTQ1PnEJFmg1/77PEa2gdxuTWyU6IwBklE02x9vpq6UYl+t4nPdZ4mg8znf7opaBUMgAxrJN+cVIm7emPQcfB1VBzdfqIYkBOzJ+6oeFspcycno3WdBHy3nKUgHRU7++z0DjpJTYjwqy89wuny0tgxhNmokJkc/PZpU+cQDqeXrOSooJU0BoZdtPXYiI82Be1SqWvahPPUBjrRbL2+rnOWwK0lZzJPb08TuOzIidlIBhFQXAi6rvt+Lr1u3/UQ5M6VNtzr2xoRlYQcFbh3Wfd6fP/+ioqcGLxjYFuPjUGbi/SkSCLNga+hO4fx9rYgm6OQ49Kwr/81ntptIedtWvIxnO//PuS4oXQZ3pbqMB0VDRhn34Vr+4thXuOjGIoX+35f6Nqpn+3A66Or307PgJOUeAuxUYE/ly63l4b2IYwGOWSjpeauYWwON5nWqOBdTR2DaH1tyBGxYRvNXKvCbf8QbcoFQRCuEsMON4/+16aQ4/HRJr5btC908xfAsvpx1Oxyv8f+/F4ta3cE3+MN8MGVRSybkXn2ExaEa4Bt7U/xNhwIOW6cdTeu3UHKNZ6iFszF21KNbu8P+RxD+c24D7wRctw0936M5WvObMLCRXVF7akWBEEQzk2k2eC37eR0BRmxKCmTQp9AVpGik3Hufw3b6z/EtvanuGu2UJAevnpSQXr49uSCcC0L2xpcMaLmzRy31Sv48eHOIcdnoqaHLqM54RyEK4YIqgVBEK4it8wPXgtWkSVfomTxIqSIuKDPUfNnY3/jR7h2/hVvcyXehgM4NvyW4mPPkpkUPFifmp/g1/RCEK43hpIb/PZq+41NWYYSn46heFHQcSkyHkPRIt8qc4jA2zj9VpTMqcjWvKDjSloJSmqYL8vCFUME1YIgCFeRBVPT+NDKImLGNWtIibfw6N3TyEuLQTJFYrn1q/4r1qoRw9SVvo6Kw4GttPWWIzxaMUhZgX+i0/wpKXz2jqkBzxeE64lsiQnsUmowYyy/GdOc+wAwLfoQhik3gTKWS6CkFvmS+IwWlJRCLCsfRYoZl4xpicW0+CMYCuchSRKWNV9GzZ0xVulDUlDz52BZ+egleZ/C+RN7qgVBEK5CHq9GQ/sQqiKRlRwVNCFL629Hdwz6KqpIkq+joh48yVG25hF557fpGXDQM+gkOc4SNNlQEK5nWl8bunMIOT4DyRgkUdY5jNbXimSJCZrEN5qMqXmRk7KRgpQS1IZ70Ye6kaKTkEPcdRIun3B7qi958xdBEATh/KmKTP4ETVHk2BQ4VaZMG+4NGVCDr+sgQEKMmYSY4LWYBeF6J8elhh2XTJFh9z9LkjRhO285Mh4iRVfOq5EIqgVBEK4yww437+9v4dCJblRVZlZxMvOnpPo1pfE07Md9dLNvpTopF0PpUqRoK/pgZ9BzKimFeNpqcFe+hz7cgxyb5tsvmjjWNGJg2MWGfc1UN/RiNCjMnZzCnNJklFNl63RdZ8/RTrYdacPm8FCYGcvS6RkiSBcEQPe4cB/b6qvOo3lRs6b59mubwndEFK4eYvuHIAjCVaRnwMH3/28vXf3+DWAKM2P5yv0VmAwKjs3P4K5c73+gasIw+Ubch9YFnlQxYChdivvQW/6PSwrmpZ/EUDiPth4bP/i/vfQP+7c6npafyKN3T0ORJX7zaiU7Ktv9xiNMKk88WEFuavhVdUG4luluJ7Y3foTWXuv3uBRtJeL2b/hWp4WrgiipJwiCcI34y4bjAQE1QG1TP+/sbsTTUhUYUAN4nHjq92Gc/zDSuIYncnw65iUfw3347cBjdC+OTX9Edzv4v7drAgJqgEMnutlyqJW9NZ0BATWAzenh6bVHz+5NCsI1xnVoXUBADaAPduLc+ZfLMCPhYhDbPwRBEK4Sbo+XPUc7Qo5vP9LOcveRkOP6QAeqNRfjQz/xJUupRpSETJz7XoVQNy3dDnqq91BZF9iCecS2I+1EB+lKOaK+bZDW7mHSEsVtbuH6FK4jo+fETvQlH0NSREh2tRP/goIgCFcJl0fD4w29zc3m9KA7bWHPobtsSIqKMq482ETH2O0OdAKri4y+rsODIoceH5mbIFyvwl5jXg943SCC6que+BcULou2Hhvr9zZR3zZITISRhWVpVBQmXe5pCcIVxdNwAPfRTej2AeTEbCxTlpOeFElL13DQ50/KjEVJLQrdplxRsUVlsnFLHZUnezGoMrNLkpmTHL5bW3JeIfHRTfQOBl+tnpQVS7TFQFV9b9Bxi0klMyl810ZBuJYpqZNCXpdyQlbQ8nzC1UcE1cIld+RkD//914O4PGPlvfbUdLJ8RiYPryy6jDMThCuHY9uf/JIKvW01uKs3sqbokzzVFfh8VZFYPTcbQ3weroNvog8HBrievEV890/VfsHx4boetmfF8NmELPSexoBjlOxyjNYc1syVef6dYwHjJoPCillZmI0K6/c2M2R3BzxnxaxMTMbQbZwF4VpnLL8ZT/0+0LyBY9NvvQwzEi4GkagoXFKarvPHN6r8AuoR7+5toqax79JPShCuMN722uBVOrxuyhv/xCOrioiNGmvMkpYYwRfvKSM31ddRMeLWr6GkFY8dZzBjKFvNc90VQVebqxoH2Jn+EGrO9LFubrKKWrQIy/LPAnDTrCzuX1ZI1Li901nJUXz5/nJSEyKIizLxxAMVfrWzLSaV2xfmcsei4O2XBeF6oSTnY1n5GHLsWJ1rKTIe05KPYiiYexlnJlxIoqSecEkdbejlB8/vCzl+4/QMHllVHHJcEK4Hji3P4j7ybshxyy1fRUoroaljGFWRyLAG31qhDXaiO4aQY1Oxawa++LNNaCF+5eemRvMvH5nt6+Y23IsUY0U2Rwc8z+3RaO4awmRQQiYetvfasDk8pCdFYjKIFWpBGM/b0+TrqJiQiSSL6+NqIzoqClcMuzPw1td4NkfgrWNBuN7orsCSeX7jzmFUWSYnNTDoHU+OtkK0FQDnoDNkQA1gP5VIOFE3N4MqT1hzOiU+Iuy4IFzPlITMyz0F4SIRQbVwSeWlx6DIEt4QdxWKsuIu7YQE4QqkpE7Cc2xL8EFJwRGby9qtJzlyohtFkZlVbGXhtDSM41aF99Z0suVQK4M2Nzmp0SybkUFynIWOPnvQ007KjKOqvpeN+5vpGXCSmhDBspkZfgF035CT9XubOXqqo+Kcyb5OjqoidhIK1watvx3XkXfwdtYhmaIwFC9CzZ2JdGpblNvjZcvhNnZXd+D2aEzJTeDGGRnERIxtx/K21+KqXI820IEcbcVYugwldVLY123uHOLdvc00dQwRG2lkcXkaZQVjyfu624n76Pt4Tu4FXUPJKsc4WXRjvNKI7R/CJffsuqO8t6854PHEGBPf+fhcLCbxXU+4vuluJ8N//VbQluLe/IX8R00p3QP+e6MLMmJ44v7pmIwKf3yzmvcPtPiNG1WZpdMzWLcrMBnRqMosqUjnnd1Nfo/LksTHbilhwdQ0WrqG+eHzexmw+d9NmpKXwGP3lInAWrjqeVqqsa/9KXj8ry21aDGWGz+Oy+3lxy/u51hTv994fLSJrz88A2ucBVflepybnwXGxyMSpgUPYZy6Iujr7q/t4n/+fiigXObK2Vk8sHwSunMY22s/8NWWH3/WmBQibv8n5Ii4c33LwjkQHRWFK8pDKyaxak6WXzWAyTnxPPnQDBFQCwIgGUy+ZMOMKTBSH1o1YZi6kheH5gQE1ADHmwd4e3cjVSd7AgJq8NW43nusk4dXFhEbObaqlmmN4iM3l/DuaQE1+BKLn32rBrvTw/Pv1AQE1ABH6nrYfLD13N+sIFwBdF3HsekPAQE1gKdmE56mw7y7tykgoAboHXTy5/W1aPYBnFufxz+gBtBxbn8BzdYXeG6vxh/frA5af/6tXY3UtQ7gOvBGQEANoA+049r9tzN9i8IlICIY4ZJTZJn7l03i9oV5dPTaibIYSIw1X+5pCcIVRY5OIuKWJ9GGutHtg8ixKXhkI7t/8n7IY7ZXttMZYnsHQGefg+zkKH70uQU0dw5jNMikJUby6taTAWHACKfLy7bDbVSdDF6DGmD7kTZunJ5xpm9NEK44Wmcden97yHH3sW3sqCsPOb6/touhY51IWogmR5oXz4ldAavVVfW9DAy7Qp53+5F2buvYHnpetTswLf7o6PYU4fISQbVw2VhM6oSJVoJwvZOjEiEqEQCXwx0yHwF8yYZ2V/hkYLvTg6r4JznaJ+h2OGh3hQy6AWwTJCALwpVOd4X+MgqAyxa2K6hX03Ha7YRbHtJdgV0VJ7r27E5P0ONGeZygayCJKiJXAhFUC5dFS9cw7+5toqFtkOgII4vK0phRZL3c0xKEK1qk2UCGNZLmzuAdFYuy4shPj2F3dUfQcVWRyYmTcO7+O57mI0iqETVvNpPSJ7M2zOtOL7Ty/oHWkB0Vi0WCsXCVU6y5oBjBG3zVWEkrosgdR1d/W9Dx1IQI4rIzcRwI9xr5uA6/4+usqHlRsqaRn7UAWZJCVuYpyopDUYrwNgQ/sZxcIMryXUFEUC1ccodPdPPffzuEe1wDmP21XaJGtSCcgVvn5/LrV44EPK4qMqvnZGONs7BuZwM9QfZd31gah/Lmv+Mat7fT21zJpJQispOX09ARGKxXFCaRnRrNLfNzeO6tmoBxs1HhptmiRJhwdZNMkRimLMN9MPDrpRQRh6F4CatTdXadqvpxutsW5GJIS8WdPhlvS1XAuJxSiHP339A660Yf87Yfw1y9kQUlH2JzVU/AMcnxFuaWJiMl34q98TDop98RkjBNv+3s36xw0YhEReGS0jSdP66tDvpLacM+X6kuQRBCm1uawkdvLiEhxjT6WIY1ki/dW0ZOajQRZpWvPjidyTnxIymOWEwKN8/L4Xb5PfQgyVJ6ew2PTutlZrEV+dTeTIMqs6Q8jU/fMQWAZTMyefCmScSMS3LMTY3mK/dXiLrUwjXBNPc+jNNvA6Nl9DElfTIRt30dyRRJZnIUj99bTua4Zkvx0SY+vLqY+VN9nRItK7+IOmkByKfWLGUFtXAeSmqRX0A9Qh/q5r7I7ayYlTXaKEmSoKwgka8+OB2DqqCmTsKy6lH/bozRVszLPo2aU3ERPgnhXImSesIlVV3fyw//FKajYkU6j6wuuYQzEoSrk6bptHQNoyhSyM6GPQMOhuxuUuIjMOJk6OnPQ4hf+XJSLpF3/SsDwy56B50kxZmJNBsCnufxarR0DWMyKiKYFq5JuseJ1teGZI7y5TQE0dZjw+3RSE+KQJED1yd1xxDaUDdSVAKyOZqhF7+OHmLrCLJK1Ed/hcMDnX12oiOMxEebgj7V29sCuoYcn44kiXXRy0F0VBSuGI6JkqgmGBcEwUeWJTKTg/9iH5EQYyYhxpc6pQ07QwbUALrbl6gVE2n0W40+narIZKeIBGPh2iWpJpSknLDPSU0I/4VSMkehmMddn+ESITUPeN1YTJYJry0lPj3suHB5ia85wiWVlx6DqoQu/VOUGXsJZyMI1w8pIg4pJjnkuJpadAlnIwjXFyUt9PUlJ2YjjdtyIly9RFAtXFKxkUZuKA9ezzYp1jy6L00QhAtLksIkNalGDGWrL+2EBOE6Yiy/BZTgmwOMItnwmiH2VAuXnKbp/O39E7y3rwm704sETMlP4JGVxSTFiW/rgnAxuao24Nr7Cvqwr9qAnJSDacHDYqVaEC4yT3Mlzm3Po/X4updK0UmYZt2FYdKCyzwz4WyE21MtgmrhsnG6vHT0+ToqhkrKEAThwtM1Da2vBUk1IofZEiIIwoWn9behe73I8Wki2fAqJIJqQRAEQRAEQThPovqHcMVp6hzi3T1N1LcNEhNpZNG0NGaViBUzQTgb7b023tndxPHmfiLNKgumpjG3NAVZDp0M3Dfk5N09TVSe7MWoyswqSWZJeRoGNXRXNpvDzXv7mjlQ242OTkVhEkunZxARpOSeIAjC9UqsVAuX3MHj3fzib4fweP0bwCwpT+MjayZfplkJwtWlprGPn/75AE63fxnKWcVWPvOBqaNNXMZr67Hx/f/by8CwfyvmwsxYvnJ/xWjzifEGbC6+/9xe2npsfo+nJETwTw/PCFt+TxAE4VoTbqVabOYRLilN03l6bXVAQA3w/oFW0VFREM7Qs+uOBgTUALuPdrKvpjPoMS+8eywgoAaobernvb3NQY95dfPJgIAaoL3HxqtbTp7dpAVBEK5hIqgWLqmaxj56B50hx7dXtl/C2QjC1amxY4jmruGQ48GuI5vDzaET3SGP2RHi2ttRFfqa3F4ZokOcIAjCdUgE1cIlNVFHxYnGBUHwVc4JJ9h15HRr4Roq4nB5zurxUK8jCIJwvRKJisIllZ/h66jo8Qb/616cFXdpJyQIl1lHn513djdyrLEfi0lh3pRUFkxNRVVCr3lkJUdhManYncED3uKsWNy123HXbEa3D6JYc4mesoKUeAvtvcHbJRdnx3HoRDcb9jXTPeAgLTGS5TMyKcqKo/Jk8G1ZxdlxZ/1+BeFyc3u8vH+glV1V7Tg9GpNz4rlpZiYJMeaL+roOl4eN+1vYc7QTr6YxNS+R5TMzRV7CNUQkKgqX3PPv1PDO7qaAx61xZr7zsbmYjKGrEAjCteR4Sz8/fmF/wIpvWUEij949DUUOHVi/sqWOf2yqC3g8NsrIPxUdxXJyk/+AorK/8FP8YdtQwDEmg8KCqSm8t6/F73EJWDkni7d3NaGd9qdCliSeeKCCkpz4Cd6lIFw5XG4vP35xP8ea+v0ej7IY+NpD08mwBk9AO182h4cfPr+Xhg7/6y8+2sQ/PTxDND67iohEReGK8sCySdy6IJcIk+9GiSRBeUEiX31whgiohevKc2/VBN1CcfB4NzsrO8Iee9uCXO65sYDoCF9ZOwkozY3nK0ujAgNqAK+H6c0v8tE1xSSOW5HLS4vhozeXsOG0gBpABzbsa+GTt5WSnhQ5+nh6UiRfuGuaCKiFq87G/S0BATXAkN3NC+trL9rrvrWrISCgBugddPLXjccv2usKl5bY/iFccrIscdeSfG6dn0Nnv4Moi4FYcftLuM6099iobxsMOb6jqp35U1NDjkuSxM3zclg5O4v2XjsRJpX4aBP2DU8Rahe0PtTN/JRhFn52Pu09NgyqTFKshVe21BHqPp/T7cWraXz3E3Np77WB7iunJwhXo3CJt5V1PQzaXERHXPi/R6ESgQH2HO3E49XCbvkSrg4iqBYuG6NBIWPc6pcgXE8mTNoNsV/6dKoi+19HHkf4A1wOZEkiLXHsmDNNfEyJF8G0cHUL97Ou4/sSGX0RXtcRpPzlCK+m4/aIoPpaIIJq4bJoaB/knZGOihEGFpWlM2dyMlKQhhWCcC1KT4okOsLAoM0ddLw4O56dVe1sPthK/7CL7JQobpqZRU7q2J/81u7h0Y6KEac6Ks5OLYYTu4K/qGJkMCKTd96rpfJkLwZVZnZJMvnpMWHnmpUcxatb6thf2wXg66g4I5Moy1hHxX01nWw80ELvoJOMpEiWz8qkID32LD8VQbi4irLjQpajtMaZcTg9/PHNaupaB4iyGFg0LY25U1JGmynpbifu6g24T+wCrxs1YwqGqSuQI8e2QlXV97J+bxMdvXascRaWzsigOCuOnVXBt3TlpEZjHGjCfvhttJ4GpIg4DMVLMOTPHn2O7rLhqlyPp24voKNmlWGYshzZMnbtehoO4Kp8D324Gzk2DcPUFaipky7ApyacKZGoKFxy+4918T//OBRQAWThtFQ+fkvpZZqVIFx6a3c08Of3AvdxRkcYKM1NCLhlrMgSn7ljKjOLrRxt6OWnfzmAy+3fSGl2YQwfcr+IPhjYAMY16Sa+cygnIJDPS4tG1+FkkO0o5YWJtHbb6DitakhynIV/+uAMYqNMvLj+GOt2NvqNSxJ8/JbJLJiaFv5DEIRLqL3Xxnf+uAu7M3DleNWcLNbvbcbt8b+m5kxO5tO3TwGPE9trP0Dr9E8QliwxRNz2DeS4VN7a2RB0b/aKmZlsONAScG4J+MxCMyXVT4HuPyfD5KWYF38Y3TGE7ZX/h9Z3WiJxVCIRt38TOSoB566XcO179bRXlTAt/jDGyTeG/1CEsyISFYUrhlfTePato0FL6m051EZVveioKFw/Vs/N5sHlk4iNGtvDOTknnvuXFQbdg+nVdJ576yger8azb9UEBNQAu2oHqJ38CZTscl9kC0imKIwzP8AzHVODrozXtQ5SXpjI/CkpqIrvGJNBYfnMTGKjTAEBNfhKAb6y9SSNHUMBATWArsP/vX1swq0lgnAppcRH8MQD0ynMGLuLkhhj4sOri9lb0xkQ9ALsrOrg4PFuXIffCQioAXT7AM4dL9I/5OQvG4InHa7f18wnby31u9OUEm/hU7eVMLnuTwEBNYC76j287bU4978WEFCDL0fCuftvaH1tuPa9FuRVdZzbnkd3BXZEFS4Osf1DuKRqGvvDdlTcUdnOZFFRQLiOrJidxdIZGXT22bGYVOKiTDz71tGQz+8fdvH+gRZawnRU3HLCRcVdj6PZB8A5jBSViN0jcfidIFVBTtl3rIt//egcHlpRxMCwi/hoE2ajyqP/9X7IY3ZWtmMxhv4zYnd6OHiim9klySGfIwiXWl5aDN/40Ex6Bhy4PBrJ8RbqWgbo7Audj7Czqp1C5/aQ456GA+yNb8Ib4o65V9PpGXTy7Y/MpqvfjlfTSY6z4G08iN0ROmHZfXwHnrrdoV/3+E7k6CQIlWrsceE5uQ9D0cKQ5xAuHBFUC5fURKtWzjDJHIJwrVIV+awSB22O8EmMI9eRbImBU3sunXZn2I6KzlOr3pFmA5Fmw7jHQ8/F4fJOeM2KlWrhSjW+2Uu4RELw/azr7tALQugaHleYccB5qjtpUuxYTeqw5wRwO8I/x+tCd4dPTtY9E7yGcMGIoFq4pAoyYlAVGY838BYbQIno0CYIFGfHsfVwW9AxVZGYW5rC2h0N2EJUCCnKjmPb4TY2H2pl0OYiNzWGm2ZlkpoQQVtP8FvBJdlxeOr34aragD7UjRyXhmHKTRRnx3Okrif4MTnxlGTH8e6ewGZO4Nt9UiSuaeEq8P/Zu+/oNq4r8ePfQQfYe6+SqEIVqlC9WLJcZFnucYvtOE6cZJPd/NJje52yac5mnXiTzXrTi2PHvTe5SJZVrd67KHax9wKizfz+gEyZwgBUIUGRvJ9zco4zDzO4EDnExcN79+alRmM1G4N+SJyQE4epawKeo4F7FQAMcRnk52fARv37FmB8ugPXnrf8M8+qD1PWFIz5xaAYdZd/ABjTJ6K5uvCW7dIfTy3AlD4Jz77VQZ/XmDYh6JgYWLKmWoRVlMPCshkZumPJcXbmFgavyyvEaDFnYgqpQWpBLynKICnWztVzsnXHYyMtlNd08Mc3DnG4vIWqhi427q/hJ0/sYNrYBN1zbBYjl1kP4Xzn1/gq9qI2V+E9uR3n6z/n6vRmjIbAqjxGg8K183KYPi6J7BT9TTvzClNJlk5xYhhw2ExcUZypO5YQbWPB5DTMU1eAyar7GMuM6xmTHsPk/Hjd8YlZ0WTs/B/c255DbTiJ2lSOe88b9Kx+DNOY2brnGGLTMeUXYym6Fgw6c6CKgmXGdRizpmBIHqN7DVN+Mca4dN0xMfCk+ocIO1XTeH1TGWt2VtHp9GBQFKaNTeCuK8cTF6X/B0uI0aa108VT7x5j9/FGVE0jwmZi2YxMrl+Yh+F0krt6awXvbKugrcuNAhTmx1M8Ppm/vn1E95oJ0VZuWJTPa5tKe9ePjs2I4bY5cSSv+zG66zJNFkoX/IAXNlZT1eDvCJeZFMEtl41l6hh/kt7p9PDUe8fYcaQen6phtxpZMi2Dm5bkS+1dMWxomsZbH5Xz7vZKOro9KApMzU/g01cW9C7Z8NWdoOejZ1Dr/BU+lOhkrLNuxDx2HuBfLvXs2hNsPlCD26NiMRmYW5jKjbEHMew/uzqHnzGvGGNMCu5Da8HdDYoRU+50rAvuxuDwb6j0Vh/C9dGzqE3lABji0rEU34I5d4Y/dlcXPZv/iffkVvB5wWzDPH4R1jm3ohjNus8rLkyo6h+SVIsh4/GqNLf3EGE396l3K4Q4o9PpodPpISHaitlkDBj3+lSa2nqwW01ER1j485uH2LQ/+FfQ371zOgVZsdS3OrGYjMRFWXHtfBX3zpeDnmO77H7MBQtobPVXAUkMMvvc1eOho9tDXJQVqzkwViGGA69PpbGtB4fNRHSQ7opqZzOoXpSoJN3+Ck6Xl7YuNzERFuxWE53PfBetPUhXRYORyM/+DjQNrbMZxRaJYtNP2tSORtA0DNFJuuOauxutux0lIg7FLJNUgyFUUi1rqsWQMZsM0u5YiH5E9vOh02Tsex/pldn7JJdHRVGUvt0R+9nI9PFGp2DJ9MfO3uQoxHBkMhqCLr/6mCFSf5nHx+xWE3brJ1KsUPeY6gPVh2K2ocSGXgLpr/QRnGJxoFjkfXWoSFIthBAjyITsWLYf0e/cZjEZyIvx0rPlaXzVh8BoxjymOOh6zI/JRichLo4xfQLeE/ol+QxJeShmm+6YGF4kqRZCiBFk3uRU3tlWSX1rYMOWZYWxKG/+GI+rs/eYq+EkSmIuhqR81IaTAeeY8mfLRichLpJl2kp/i3Gf+6wRBeuM64YkJjHwZAeJEEKMIDaLie/cOZ3p4xIxnF7rGeUwc9PifFaq76N9IqH+mNZYhjFrKqaChWA8PdditmGechW2pfeHM3whRiRjQhb2ld/CkJzfe8wQm4Zt+Zcx5UwfwsjEQJKNikIIMUJ1Oj1093iIj7Zh9Drp/Pu/EqzzmiEhm4ibf4TmdqI521EiYlGClA8TQlw4tasFVC+GKP3NhuLSJhsVhRBiFPrkJkfV5SFoK2NA8/q/llYsdhSL1JYWYrAYIuKGOgQxSGT5hxBCjAIGRyyG2LSg46b0iWGMRgghRh5JqoUQYpSwzLxBf8BsxzL1qrDGIoQQI40s/xBCiFHCPGYOaBqunS+jtfkbURjTxmOdeweGmND1cYUQQoQmGxWFEGKU0TQNrbMRjJbeNshCCCH6JxsVhRBC9FIUBUUqDwghxICSpFoIIUYRtb0B9/53/B0VTWZM+cVYJl0uFT+EEOIiSVIthBCjhK+5ku7Xfw6urt5j7sZyvCe347j2AUmshRDiIkj1DyGEGCVcW57pk1B/TG0sx31wzRBEJIQQI4ck1UIIMQpori7/ko8gvCe3hTEaIYQYeSSpFkKIUUDznVtHRSGEEBdG1lSLIVFyqo33tldSXttBVISFhVPSWDg1DYOiDHVoQoxIBkcshrh01JZTuuOmjElhjkiIS09ZbTvvbquktKadSLuZBVPSWDQtDaPBPwfZ3eNlza4qdhypx+NVKcyN58rZWSTFDu5+BLWnA8/+d/GW7QZNxZg9FcuUq6Tl+SVG6lSLsNtxpJ7fvXoQ9axfvdkTk/nidYUoklgLMSg8J7fR8/7jgQMWOxE3/QeG6OTwByXEJWLP8Ub+9+X9+M7KJaaPS+QrN02hx+Xj50/toqqhs894hM3EA5+eQUaSfu3ii6V2t9H92k/R2uv7HFccsTiu+3cM0VIeM5xC1amW5R8irLw+lSffOxaQUANsO1zPwbLmIYhKiNHBnD8b2/IvY4hLP31EwZgxCce1D0hCLUY1VdV46r2jAQk1wO7jjew93sj7OyoDEmqArh4vz31QMmixufe8EZBQA2jdrbh2vDRozyvOnyz/EGF1rLKV9q7gaze3H65ncl5CGCMSYnQx58/GnD8btasFxWhGsQ3O7JoQw8nJU+00tbuCjm8/Uk9lfWBC/bEDpU04XV7s1oFPq7wntwcfK92Opn1BvuG9REhSLcLK7VVDjnv6GRdCDAxZiynEGW6vr59xNeT7k6aBx6cyGCur/ZuMg/B5QVNBMQ7CM4vzJUm1CKuxGTFYTIagyfXEXHmjF2Iwqe31uPetxlt9EMVo8XdUnHxFb+MXr0/lwz2n2Hygli6nh/z0aK6cnUVuavQQRy7E4MlLi8ZmMdLj1k+uJ+XGEeUwU7/HqTuelRxJfYuTJ989RkVtB1ERZhZOSWPR1HQMBv8sstbTifvAe3hLd6CpPkxZU7BMvRpD5JlvZz0nt+M5/AFqewOGmBQshcsxpU/EW7pD93mNaRNQDJJQXyokqRZhFWk3c0VxFm9uKQ8YS0+MYM7ElCGISojRwddU4e+o6O4G/AX23M2VeEu341j1EJrJxm9f2s++kqbec+pbnew4Ws9XbpzCtLGJQxS5EIPLbjWxYk42L28oDRhLjrMzf3IqbZ1uth2ux+ny9hlXgMLceH7+5K7e/UL1rU5Kqts5WNbCv1xfCK4uul/9CWpbbe95nrZavCc+wnHdQxhi03BtewH3njd6x30dDTirDmAqvByMFvCdtXRSMWKZef3A/SOIiyYbFUXY3bQ4n1uXjiU20gKAyWhgbmEK37ljOhazfOIWYrC4PnqmN6H+JLWpEveB99h5rKFPQv0xr0/jqSAbjIUYKVYtyOOO5eOIj7YCYDQozJ6YzHfvnIHNYiIl3sF37pjOpNw4Pl7BnJkUwZeuL2TzwVrd+2PHkXoOljbj3vtWn4T6Y1pPB65tL/i/Qdrzpm5c3sPrsC3/F4ypBb3HDEl52Fd8HVP6xIt/4WLAyEy1CDtFUbh6TjZXFmfR1uXGbjVis8ivohCDSevpxFd9OOi4t3Q72435Qccb23ooq+kgP12WgYiR64pZWVw+I5O2Ljc2izFg42FOahTfun06nU4PXp9KbKSVg2XNoTfgH6knty3EZsPy3RgScwjanEn1oXW14LjuIdSeDlBVDI6YC3l5YpBJJiOGjMGgEBdlHeowhBgVNNVL6I6KHrxafxuJQ2/mEmIkOJf3pki7ufe/+9tg7/GqEGqzoaZCfx1NT48bbFGhHyeGlCTVQggxwvhUlfV7TrFxfy0d3W5yU6O4cnY2aXEZqC3VuueYMgqZaItjz4lG3XGH1URumsxSC3G2cZmhN+BPyo3H2FSI9/gm3XFD8hiM2dPgE+upz2bMLByQWMXgkjXVQggxgqiaxuMvH+Af7x6jtKadxrYedhxt4OdP7qIybRmgU8/WGoFl6lUsnJJGcpB2y9fMy8Eqex6ECBBhM3Pl7CzdsYzECOZMSsZSdA2YbYEPUAxYZ96AKXUcxqwputcw5RdjjNe/vri0SJtyIYQYQfacaOQ3L+zTHYuNtPDINVa8u15Fba4CFIyZhVjn3tb7pt3S4eLZtcfZebQBn6qRGGPj6jnZLJuRGcZXIcTwomka726v5N3tlbR0uDAZDRRPSOa2ZWOJjvBvyvc1lOLa+hy+U/69DYbEHKyzbsKUPc1/Da8b17YX8BzdAB6n/8PuhCVYZt2EYpSFBZeKUG3KJakWQogR5E9vHGLzgcAqAx/7zh3TmZATh9rdhmI0oVgjdB/X4/bS4/YRHWHBIN3ahDgnqqrR3u3GbjFhteh/s6O5utBUHwa7/nIqzedB6+lEsUVJMn0JCpVUy09LCCFGkH43Tfn84/1VD7BZTFKVR4jzZDAoxEaG3uSoWCP0FmGdGTeaUaTj6bAkfzHFkDhW2cq72yspr20nymFh0dQ0lhRl9HaeEkJcmMK8eLYfqdcds1qM5Ef20LP+r3irD6EYzf6OilOu7J2x1nxePIfW4jm2Cc3ViTF5DJapV2NMDl5uTwjR//ua2tOBZ987Z3VUXIEhOmmIIxcDRZZ/iLD76FAtf3z9EGf/5s0sSOLLN05Gka+ahbhgLo+PH/1tOzVNgU1eVs2IY3n1H/zrNT/BEJeJ4/qHwGTFufoxfFUH+p6oGLFf8a+YcqcPZuhCDFv9va9pPR10v/ZTtLa6PuOKNRL7dQ9hjEsPY7TiYoRa/iHVP0RYeX0qz7x/POAPD8DOYw0cKG0Of1BCjCBWs5Hv3jmDeYWpmIz+P/EJ0VbuXD6Oq9zvBiTUAGpLFe4D7+Mt3RmYUANoPnq2PIXWTx1rIUajc3lf8+xbHZBQA2iuTtzbXwhDlCIcZPmHCKujla20dwcvgr/9SD1T8hPCGJEQI090hIX7V03inqvH0+P2EeUwo7i66NwVoqPiye0YYlKCjmsdjagNpRiTxwxGyEIMW+fyvpYXsqPiHjSvG8VkGYzwRBhJUi3CytvPJqr+xoUQ585qNvbWllZVb+gH+zxovtCP6W9ciNHonN7X+uuoODJW4o56klSLsBqXGYPFbMDt0f8jNCE7lre3lrPlQC3dLi9jM2K4anY2edLJTYwiO47Us3ZXFXUtTpJi7SybkcHsicFnkc+FwRGLIT7zdH3qQMbMyRhiUvFV7NG/gDWCOiWJ1W8c4khFCxazkTkTU7iiOAu7Vd5KxOjV3/taYV48xqbJeI9t1B03pIzlcHUX724/QmV9J7GRFhZNTWdxUbqUsxxmZKOiCLtXNpzktU1lAcczEiOw20ycqGrrc9xkVPi3m6fKshAxKry8/iSvby4LOH7N3Bxuuezill54ynbS8+5vgbP+VlojiLjphyjWSLpe/qHu2s+OCdfz4+3xuDy+PsezUyJ54NMzpPyeGNWCva9lJkXwvc8UY+ispfvlH4Gnp+8DFAMnxt/Lbzd7z74rmT0xmS9dP3nQYhYXRjYqikvKDYvyuWP5OBKi/bU8LSYDC6eksaQoPSChBvD6NP75/nFGyOc/IYJqauvhzS3lumNvby2noTVwk+H5MOfOxH7lVzEk5PgPKArG7Gk4rnsIQ1QSisWOY9WDmMbOg9NNJ5ToZKyL7uWvZVkBCTVARV0n63afuqi4hBjugr2vffuO6ZhNBoyx6ThWPYgxczKcrlJtSMrHdMX/4887Az7mArDtcD1HylvC9yLERZOZajFkVE2jy+nBZjFiNhn57+f3sq+kKejj/+O+2WQl6386FGIkeG97JU+vOR50/NalY7l6TvaAPJfW0wlGE4rZpj/udaN5XSjWSFo73XzzfzcFvVZ+ejQP3zNrQOISYjg7+31Nj+Z2gupDsUWy+3gD//Pi/qDXWzojg7uvHD9Y4YoLIB0VxSXJoChEOc7sdvb6+tns0c+4EMOdVz23bogDQbGF/oCqmCy91Qj6vTdlg7EQQOD7mh7FYu/9b58v9GSg3FvDiyTVYsB5vCprdlax+UANnU4PY9JjuGpONmMzzrRFPlLewrvbKymrbSfaYWHB1DQm5cZxqEz/q67oCIvMUosRrzA3nucpCTqenxbN8x+cYOfRBnyqyuT8BFbMySY5ztH7mC0Ha/lgdzWNrU5S4hxcPjOTWROSe8d9radw73kbX/VBMJkx58/GMvXq3o6KevfvlbOzSIl3UNcc2FAGYLLsdxAC76nDuPe9g9pYhmKPxjx+EeZJy1AM/hnr9i4372yr6HP/LpqahsloCPrBVfYSDS9Dsvxj7dq1/Pa3v6W7u5uFCxfy8MMPs3nzZh555BFcLhcrVqzg61//+nldU5Z/XBp8qspjz+0NSI6NBoV/uWEyMwqS2HKglj+9Gdh5anJePLXN3TS2nbWRA7j7ygKWzsgczNCFuCT83ysHdNuMT81PoKa5i4bWvvdHhM3EA5+eQUZSJM+uPc472yoDzr1uQS43LMrH11BK9xv/GbBZyhCXjuO6f0c124Pev8tnZfLutsqAtZ/RERZ+cG8xcVHWC3vBQowAnuOb6Vn3x4DSeKac6diu/Dc6ur389B87dO/fGQVJbNhXE3DN3NQo/v2emRgNsv3tUnJJbVSsrKzkBz/4AY8//jivv/46hw4d4sMPP+Shhx7i8ccf56233uLAgQN8+OGH4Q5NDIAdRxp0Z5t9qsbT7x/D5fHxzFr9zlMHSpu5fmEucyalYDL6N3Kkxjv43MqJklCLUeP+VZO4dn4OkXYzAJF2M9fMzSE13hHwhgzQ1ePlxQ9PUtvczbs6CTXAm1vKaelw4fro2cDqA4Dacgr3gfdD3r87jtTzpRsm935jZFAUZhQk8eCnZ0hCLUY1zefBteVp3VrT3vLd+Cr3s3prRdD7t73LzW3LxhJ/epOj1WzksqJ0vnV7kSTUw0zYl3+89957XHPNNaSmpgLw2GOPUV5eTk5ODllZWQCsWrWK1atXs2TJknCHJy7SzqOBM2wfa2p3sX7PKTpCdJ46WtHGF68rxO2ZgNur9iYWQowWJqOBmxaP4fqFeThdPuxWI0aDge/83+ag5+wraSI3LVK3ggD4k+Jdh6qYXXMk6DW8pdvZacwPOt7U7iI+ysp/3Debrh4PJqOht7GMEKOZr+YoWk9H0HHPyR3sODox6Pj+k818+cbJXFGcRXePF5vFiMkoyfRwFPakury8HLPZzOc+9zkaGhpYunQp48aNIykpqfcxycnJ1NUF1kkNJdhUvAgvY5Ddzh+z9NMkwmQ2kpQUNZAhCTEihFrcpmoaFkvozVEWS+h706io/d6/kVE2kpKiSAr5KCFGl+5WC6GKXdosSr/3b3x8JDZpojTshf0n6PP52LFjB//4xz9wOBx8+ctfxm63BzxOOc8uQrKm+tIwLiOarQdrdccibCam5cdjNRt1690CjE2PoqEh+Cd+IUariTlxbNRZdwkwNjOGcRmhP4zmZyVhKMlCbdZfIkJaIeNsoe/fOLtJ7k8hzqI5MsFkBa9Ld9ybOL7f+3fL3ire3VZJZX0nMac7Ki6dnoHBIB0VLzWX1JrqxMRE5s2bR3x8PDabjcsvv5xNmzbR2NjY+5j6+nqSk5NDXEVcquZPTiUtwaE7tmp+LrGRVlYEqbObnRxJ8YSLa8UsxEh1zdwc3XbgRoPCjQvzGJMeQ9HYRN1z5xamkJEYgbX4JtCZsFBsUVimXNXv/WuR5R5CBFAsDizTrtEdMyRkYxozO+T9m58WxWPP7uVAaTNtXW4q6jp56r1j/O7VA4MduhhgYU+qly5dysaNG2lvb8fn87FhwwauvvpqSktLKS8vx+fz8cYbb7B48eJwhyYGgM1i4jt3zmDB5FTMJv+vV0q8g89eM4ErZ/uT6esW5nHXlQUkxfqbTljMBhZPS+dbpztPCSECpcY7eODTM5g6JqE3Lx6bEcPXb53GxNx4AP7lhslcM/fMJsdoh5nrFuTyuZX+9ZymnOnYr/oahqQ8/wUUg//YdQ9iiEo8p/tXCBHIOvN6rAvuRok6vTjKZME8YQmOld9BMZqD3r//etMUNuyr1V0esuNoA4fLmsP2GsTFG5KSei+88AJ/+9vf8Hg8LFiwgIcffpitW7f2ltRbsmQJDz744HktAZHlH5cej1fF4/Vht5p0f5aapuF0ebGYZVOGEOfD5fGhqpruzBf4S1v2uH3YLaagXx9r7m4wmHobvJytv/tXCBFI0zRwd4PJimLUvz8/ef9KR8XhJ9TyD2lTLoQQQggxBHYcqefxV4Iv81g8LY17VwSvHCLCT9qUCyHEKOL1qby/o4pN+2vo6HaTkxrN1bOzepeJCCEGnrfqIO79q1Eby1Fs0ZjHL8Q8eTmKwZ9qtXW6eHtrBTuP1uNVNabkJbBkerp0VBxBZKZaCCFGEFXV+O/n/ZuePklR4PMrJzFvcuoQRSbEyOU5tpGedX/m7OKXxuxp2K/6f7R3e/npEzsCOgY7rCZmjU9ivU5lkPz0aB68a4Y0gLnEXFLVP4QQQgye3ccbAxJq8Dd7e/aDE0FnxIQQF0bzunFteQa9avK+ir34Kvbx9kflAQk1QLfLS1uXmzuWjyMxxr9532YxsmxGBt+8TToqDjf9Lv8oKSlh165d3HLLLXzlK1/h6NGj/PSnP2Xu3LnhiE8IIcR52HUseFfT9i43xytbZRmIEAPIV3sMzdUZdNxTuoNdxyYFHd93sokv3ziZ5TMzcbp8WC0GSaaHqX5/aj/4wQ+wWq188MEHtLS08LOf/YzHHnssHLEJIYQ4T75+lsH1Ny6EOE+qt59xX8j7TtNAVf1N7xw2kyTUw1i/PzmXy8V1113Hpk2bWLFiBXPmzMHj8YQjNiGEEOcp1MYmu9WI3WriD68d5Gu/2cC3H9/Mc2tP0NHtDmOEQowsxtTxYLYFHTdlT2VKfvBvhwqyYrFapLHSSNBvUu12u2lsbGTdunXMnz+fxsZGXC79VpxCCCGG1uyJyWQn62+imVeYyqPP7OGjQ3W0d3toau9h9bYKfvbkLjqdMlkixIVQLHYsRSt1xwyJOZjyilkxNwdHsI6oi/IGO0QRJv0m1bfddhtLly5l5syZjB07lltuuYXPfOYz4YhNCCHEeTKbjHzrjulcVpTeO/uVGu/g3hUTqKrvxOXxBZxT19zN+zsqwx2qECOGdfoqrIvuRYk5XV3HbMM8adnpjoomUuIcPHDXDIrGJmI43UxpfFYs37ytiPHZcUMYuRhI51RST1VVDKfX+LS0tBAXd+n9AkhJPSGE6EtVNdxeHzaLiU6nh6/+ekPQx2YkRfDjz80JY3RCjEyapweMFpQga6M9XhVV07CaZcnHcHRRJfW6urr4yU9+wmc+8xlaW1t57LHH6OrqGvAghRBCDCyDQcFm8X/l3N8GRZmUEGJgKGZb0IQawGwySEI9QvWbVP/kJz8hKiqKpqYmrFYrnZ2dfP/73w9HbEIIIQZITISF7BT92RWQzm1CCHGx+k2qDx8+zNe//nVMJhN2u51HH32Uw4cPhyM2IYQQA+imxWN613N+UnSEhSuLs4YgIiGEGDn6TaoNZ32F4fP5Ao4JIYS49E0dk8DXb5vGuMwYwF95oHhCMg/eNYP46OAlwYQQQvSv346KxcXF/Nd//Rc9PT1s2LCBJ598kjlzZDOLEEIMR4W58RTmxuPx+jAYFGk0IYQQA6Tf6h8ej4c//OEPrFu3Dp/Px6JFi/jyl7+M1WoNV4znRKp/CCGEEEKIwRSq+ke/M9Vms5nZs2fzla98hdbWVnbs2HHJJdRCCCHO0Lxu3PvfwXNsI5qzA2NSLpapKzBlTRnq0IQQYsTq93u/xx57jN/85jcA9PT08Ic//IHHH3980AMTQghx/jRVxfnOf+Pe/iJaWx24u/FVH8L59q/wHA1ep1oIIcTF6TepXrNmDX/5y18ASE1N5cknn+Stt94a9MCEEEKcP2/ZTnzVh3RGNFxbn0PzSTtyIYQYDP0m1R6PB7PZ3Pv/zWYzik5JJiGEEEPPW7Yr6JjW04Gv9ngYoxFCiNGj3zXVM2bM4Jvf/Ca33HILiqLwyiuvMG3atHDEJoQQ4nxp6sWNCyGEuCD9Vv/o7u7mN7/5DZs3b8ZkMjFv3jz+9V//FbvdHq4Yz4lU/xBCCPAc20TPuj/qD1rs2K/+Bp797+CtPoRismDKL8ZSdC0GR0x4AxVCiGEoVPWPfpPqX/7yl3zzm98clMAGkiTVQggBms9D96s/RW0sCxgzFS7He+RDOGtdtRKVhOPG72OwRYUpSiGEGJ5CJdX9rqlet27dQMcjhBBikChGM45rv4N58hVg8X+jaIjPxHbZ/WiN5QEJNYDW0YBn/7vhDlUIIUaUfmeq/+Vf/gWXy8WMGTOIiIjoPf7Zz3520IM7HzJTLYQQfWmaBqoPxWhC7emg64l/C/pYQ1wGEZ/6aRijE0KI4eeimr/ExsYCUF1dPaBBCSGEGFyKooDx9J95tb8NjDIpIYQQF6PfpPqRRx4JRxxCCCEGkcERgyEpD7WhVHfcmD01zBEJIcTI0m9S/aUvfUn3+O9+97sBD0YIIcTgsRbfjPPtx0Dz9TmuOGKxTLlqiKISQoiRod+k+qqrzvyh9Xg8rF27lvHjxw9qUEIIIQaeKXMy9pXfxr3rVXynjoDRiCmvGGvxTRgi4oY6PCGEGNb63ah4Nq/Xy1133cUzzzwzWDFdENmoKIQQ505TVVAU6ZArhBDn4aI2Kp5NVVXq6+svOighhBBDRzH0W1FVCCHEeTjvNdXHjh1j9uzZgxaQGB12H2tg9bYKyms7iHJYWDQ1jRVzszGbjEMdmhDDlqZpeA6uwXN4LWp7I4aYFMyFl2OZeFnvY0pr2nljcxmHylswGw0UT0xm1fxcYiOtQxe4EKNAU1sPr28uY+fRenyqxuT8BFbNzyUr2T/rqWkaa3dV88HuahpbnSTHObh8ZgZLijKGOHJxrvpd/vHyyy+febCiEBcXx8KFCzEaL63kR5Z/DB8f7K7mH+8cDTg+ITuWb90+HYNBvo4W4kL0rP8LniPrA46bp1yFbd4dHKts5dFn9uD19S2vlxhj4+F7ZhEdYQlXqEKMKs3tPfzkiR20drr7HLeajXz309PJTY3mb28fZv3emoBzryzO4vbLx4UrVNGPi+qoeOONN/bOTHs8HnJzcy+5hFoMHy6PjxfXleiOHaloZffxxjBHJMTI4Guu0k2oATwH3kXtaOD5dScCEmqAxrYe3ttROdghCjFqvb21IiChBv974ksfnqSqoVM3oQZ4b0clja3OwQ5RDIB+k+oNGzZw88038/7777NmzRpuueUW3n///XDEJkagY5WtdLu8Qcd3H28IYzRCjBzesl3BBzWNzuM7KKluD/qQPfKBVohBsyfEe9vB0mZ2HAm+V03TYPcJuT+Hg37XVP/617/mySefZOzYsQAcP36cb3/72yxfvnzQgxMjT3+1ZqSpmxCDpJ97S5WbT4hBE2p1qka/t+c5PEBcCvpNqj0eT29CDTBu3Dh8Pl+IM8RosedEI6s/KudkTQdRDjMLpqSycm4uVkvw5UEFWTHYrUacLv3foYk5sTz3wQm2HKil2+VlbEYM18zNoTAvfrBehhBh1dLh4vVNpWw/Uo/Hp1KYG8+183PJS4sG/JuV1u2uZu2uaupanCTF2lg2I5NlMzJClr8zZU/DveOlIKMKkWNnkHeggtKaDt1HFI1NvNiXRnePhze2lIe8fz0l23DvW43aXIHiiMU8fjGWaStQjOaLfn4hhpqmaXgOf4Dn4BrU9joMUcmYC5cxbWwm63af0j1nYk4cMwuSeH1Tme64AkwdmzB4QYsB0+/yD5vNxv79+3v///79+7Hb7YMalLj0bdh7it+8sI9jVW14fSotHS7e2FzOo8/u1l2z+TGbxcT1C/N1x/LTo3lveyWrt1bQ1uXG41U5XN7Cr57bw7bDdYP1UoQIm7ZOFz/9xw7W7TlFV48Xt0dl9/FGfv7ULo5XtQLw1HvH+Me7x6hu7MLrU6lp6uap947xhM7m3k8yJuZgGjtPd8w88TIMMSncvGQMRp2NwHFRVq4ozrqo1+by+PjFP3eHvH/d+96mZ83jqA0nwedF62jEveMlnO/8mvNsmSDEJcm16UlcG59AbakGnxe19RSuTU9yubKdKEfgB0ezycCNi/PJToliXmGK7jWXTM8gJc4x2KGLAdBvUv3tb3+bL33pS9x5553ccccdfOELX+Bb3/pWOGITlyivT+WFD/U3G5ZUt7M9xNow8O9k/sJ1k8hOiUQBohxmVszJZvaEZCobugIer2nw/Acl8vW0GPbe2V5Jc7sr4LjHq/LiuhLqmrv5YFe17rkf7jlFTVPg/fFJtsvuxzL7VpQo/6yzEp2Cdd6dWBfeA8Ck3Hi+fcd0CvPiMSgKFrOBBVNSeeiumRddUm/z/hoq6jsDjn98//pc3bh2vKJ7rq/qAL7K/bpjQgwXalstnkNrdceiy9bwwPVZzC1MwWwyYDQoTB2TwHfvnMHYjBgAPrdyEp+6bAyJMTYAkuPs3HH5OO6+siBsr0FcnH6Xf8yaNYs333yTvXv3omka06ZNIy5O2tmOZiXVbXR0e4KO7z7eyLzC1JDXmDsplbmTUtE0rfcr7V89tyfo45vae6is6yQnNeqCYhbiUhBqM+Cxqja2Ha4LuXRy9/FG0hIigo4rBgPWomuwFl3T5976pIKsWL55W1HQ8QsVaiNVU3sPdYd2EeUN/EDxMW/5LkzZUwcsHiHCzVu+h1CLn+NbDvKFVSthFbr3n8GgsGJuDivm5gz4/SnCo9+Z6sbGRnbu3MmSJUvYtGkT/+///T+OHDkSjtjEJarfCePzmFHu80ejv02MslNDDHP9/Qb3V2r/fJZI9PeGPOBv2Bf7d0FubzHs9XsT9P5X2O9PERb9zlQ/8MADLFy4kC1btrB161buvfdefvKTn/Dkk0+GIz5xCRqTEUOk3UynU3+2elJuPM9/cILNB2rpdHrIT49mxdycPhuhdhyp551tFZTVdhAdYWHBlDQK8+I5UNqse834aCvZyTJLLYa3orEJvLOtW3dsbGYMsycm8+rG0qDnT0s34lz3Z7ylO0D1YcqagmXGdRgTcwDQNBXPgfdwH/oAraPhdEfF5ZgnLu19ky6pbgvoqHjdgjziovzLP5wuL29sLhvw+zdlUhHde/4B3sBavQCm3KJ+//2EuJSZsotwffRs0PHOuEk89eZhdh6rx+c701FRvoEdOfqdqW5tbeXee+9l/fr1XHvttdx00004nVKEfDQzmwzctER/s2FuahTr91bz9unNSj5V43hVG//zwj427fcXtl+zs4rHXzlAyal2fKp2epNjGbuONpCRFPjVtgLcsmSMdFoUw96VxdnERgZ2LTQZFW5enE9aQgRLitJ1z10wMY7Y9Y/iPbYBPE7wufGW7aT7tZ/iqz8JQM+Hf8W15Wm0tlpQfagtp3BtfALX5qcAOFrRwn/+cxd7S5rweFW6XV4+3HOKn/1jR+/mwkef2T0o96/RFoFlxnW6r82YPhFjliz9EMObITYN84TLdMfUvPn85LVTbNxfg9Plw+1V2XWsgUee2snJU8Hrx4vhpd+k2uPx4PF42LBhA/Pnz8fpdNLdrT/TIkaPy4oy+MqNk8lPj0ZR/JsNr56dzYIpqZTV6mxWAl5YV0J3j4eX1p/Uvebx6jauKs5i+cxMImwmFMU/e/f/PjWVuf2s0RZiOIiLsvLQ3TNZOCUNq9mI0aAwJT+B79w5g/HZ/r0qd181njsuH0dKnB0FSI61c9uysdwRfwDN2RZ4Ua8b1/YX8TVV+hNuHZ5Da1Db63l+XQleX+BX1E3tLt7bXslHh2p1S+59fP92XeT9ay26Fttl92NIyAJFQXHEYpm+CvvVX0dR+n07EuKSZ110D9Z5d6LEpAAKSnQy1rm387q6mLauwG9p3B6Vl9frb/wXw0+/yz8uv/xy5s2bx8SJE5k8eTLXXnst1157bThiE5e4meOTmTk+uc+x/3lxX9DHt3W5Wb+vBmeIjopHKlr5/LWTuPMK2e0sRqbEGDv3rZzIfSsn6o4bFIUrirMCStx1PrU76DV91YfwlO4I/qSaRueJnZw8FbzCx54TjaTEBS+X2tblZsO+Uxd9/5oLFmAuWBA8ViGGMUUxYJlyJZYpV/Y5vmfjpqDnHCprwe3xYTEH7/Eghod+k+qvfvWr3HrrraSm+mcaHn30USZMmDDogYkRSjoqCjE4wnHzyP0rxKCQW2dk6Pf7NlVVefPNN3nggQfo7Ozkww8/lI6KIqhQXdmiIywsnpqG3Rr8s9z0cRff1U2IkciUUxR0zJgxCXP+rBBnK0SOndnbtVHPtLEJ/d6/i6amy/0rxAWYFuLempgbh1VmqUeEfpPqX/ziFxw9epS9e/cCsGHDBh555JFBD0wMT3MLU8gNspP55iX5OOxmblyUpztekBnD9AJ5UxZCj6VoJYpdJyk2WrAW34QxIRtTkGUV5sJlGKKTueWyMZiMgRt+46OtXDkrq9/7N0LuXyEuyDVzcoiOCNykbDndUVGMDIrWT+HT66+/npdffpmbbrqJV155BbfbzfXXX8/bb78drhjPSVNTJ2p/RV5FWDhdXl7fVMbmAzV09XjJS4vmmrk5FH1iFmv7kXpWby2noq6TSIeZhVPSuHZ+rnxaFyIEtb0B165X8JbuBNWLKXMKlhnXY0zKBU6X1Nv/7lkl9S7HPOny3pJ6J06X1DtY2ozFbKB4gr+kXny0v4ub3L9CDI6GVievbSxlx7EGfD6NKfnxrFqQS25q8G+QxKXHYFBISIjUHes3qb755pt58cUXueGGG3jllVcAWLVqFa+//vqAB3oxJKkWQgghhBCDKVRS3e9GxYKCAp566il8Ph8nT57kb3/7G+PHjx/wIIUQQgghhBiu+p2p7uzs5Gc/+xnr1q1DVVUWLFjAww8/TFxcXLhiPCcyUy2EEP3z1h7HvetVfNWHwWTGlFeMddYNGCIThjo0IYS45F3U8g89zc3NxMfHX3RgA0mSaiGECM1bfQjn278EtW8FJyUiHseN38fgiB2awIQQYpgIlVQHrf7R1tbGY489xl/+8pfeEnqqqvLEE09w9dVXD06kQgghBo1r2wsBCTWA1tWMZ/+7QxCREEKMHEHXVD/wwAMYDAaam5tRVZVly5bxjW98g/r6eh544IFwxiiEEOIiqc521Ab9FuMA3vI9WOfcGsaIhBBiZAmaVJ84cYJ3332X9vZ2PvvZz/L3v/+dpUuX8q1vfYvoaCn/IoQQI4oSWL9aCCHEuQuaVEdERKAoCjExMdTW1vKtb32Lm266KZyxCSGEGCAGezSG5DGo9SW646ac6WGOSAghRpZ+OyoCJCQkSEIthBDDnHXOrWAMnEtRIhMwT7lyCCISQoiRI2hSrXziq0CTqd9y1kIIIS5xprTxOFY95J+VNllQrJGYJy3Dcf3DGPRaoAshhDhnQUvqTZs2jezsbAAqKip6//tj0lFRCCGEEEKMJhfUUfGPf/zjoAUkhBBCCCHESHJBzV8uRTJTLYQQQgghBtMFzVQvW7asz7rqs61Zs+biIxNCCCGEEGIECJpU/+Y3vwHgn//8J2azmdtuuw2j0chLL72Ex+MJW4BCCCGEEEJc6oIm1ZMnTwbg+PHjPP/8873HH3zwQW655ZbBj0wIIYQQQohhot861e3t7TQ3N/f+/7q6Ojo7Owc1KCGEEEIIIYaTfgtQf+Yzn2HVqlUsXLgQTdPYtGkT3/72t8MRmxBCCCGEEMPCOVX/OHLkCFu2bEFRFObPn09BQUE4YjsvUv1DCCGEEEIMplDVP86pTXlZWRmtra3ceuutHDt2bECDE0IIIYQQYrjrN6n+wx/+wNNPP83q1atxuVz89re/5X//93/DEZsQQgghhBDDQr9J9Ztvvskf//hH7HY7cXFxPPfcc7zxxhvhiE2MAprPwwjpPyTEsKKpXjRNDT6uaWg+bxgjEkKI4a3fjYomkwmLxdL7/6OjozGZ+j1NiJA8Rzfg3rcataUaLA7MBQuwzroJxWIf6tCEGNG8pw7j3vkKvpqjYDBhypuFtfgmDNHJAGg9nbh2vITn+Gbw9GBIyMZStBLzmDlDHLkQQlza+s2O09LSWLduHYqi4Ha7+fOf/0xGRkY4YhMjlGvPW7i3PXfmgLsbz4H38NWV4Lj+IRSDfGgTYjB4K/fjXP3foPn8B1Qv3pKP8J06jOOmH6JYHXS/8XPU5qrec9SmCnrW/B+aqwvLpGVDErcQQgwH/S7/+N73vsdf//pXjh49SlFREevXr+f73/9+OGITI5DmduLe/ZrumNpwEu/JHWGOSIjRw7X9hTMJ9Sdozjbc+9/Bc3xLn4T6k9w7XkbzSTddIYQI5pymBP/+97/jdDrx+XxERkZy4sSJwY5LjFC+2mPg6Qk67q3Yi3ns3DBGJMTooHa3oTaWBx33VexFi6kLOq71dOCrP4kpbfxghCeEEMNe0Jnq1tZWWltbuf/++2lra8PlcuHz+WhsbOTLX/5yOGMUI4nSz5cjhnOq8iiEOAeqpuH2nJ6Z7u/eUgz93p9Kf/evEEKMYkFnqr/5zW+yadMmAObMObNBxWQysXz58sGPTIxIxrTxYI0AV5fuuDl3VpgjEmLk6e7x8PL6UjYfrMHp8pGeGMGKOdlMTy3wf1ukw5Q3E0NMKt6ynbrjSkQchuT8wQxbCCGGtX47Kj744IM88sgj4YrngklHxeHDfeRDXOv/BvT9eRkzJ2Nf8Q2ZDRPiIni8Kj/7x07K6zoCxj4328LU0ifA5+5zXIlOwXHDwyhmO843fxGYeCsKtqVflKVZQohRL1RHxaBJdUlJCWPGjOHgwYO6JxYWFg5chANAkurhxVuxD/e+t/E1VaDYojCPX4hlylUoRvNQhybEsLZpfw1/fvOw7pjdauLR27Pg4Nt4qw+imCyY8mdjKVqJwR4NgOZ14d67Gs/xTWiuLoxJeVimXYMpY1I4X4YQQlySLiip/uIXv8jvf/97li0LLKGkKApr1qwZ2CgvkiTVQggBv3v1ANsO1wcd/8Zt05iclxDGiIQQYuQIlVQHXVP9+9//HoC1a9cOTlRCCCEGhMvjw2w0YDAoGAxKyMcaFP+4x+tDURRMRlluJUQ4aT4vaCqKydL/g8Ww0m9JvZKSEnbt2sUtt9zCv/7rv3LkyBF++tOfMneurK0TQoihtPVQHW9uKaeqoROL2cDcSSlMzI7jo4P6pfGiHGa8PpWfPbmTE1VtGA0K0wuSuHlJPilxjjBHL8To4mupxr39Rbzle0BTMaZNwDLrRilTOYL0u1Hxrrvu4tZbbyUyMpI//vGPfO1rX+NXv/oVzz77bLhiPCey/EMIMZqs21PNE6uPBhxPjbcTG2nlSEVrn+MKcEVxFu/vqEI9689+tMPM9+8tJj7aNogRCzF6qW11dL3yo8DKVwYj9mu+jSl9wtAEJs5bqOUf/X7v53K5uO6669i0aRMrVqxgzpw5eDzSVUsIIYaK16fyyoZS3bHaZifTxiZw85J8UuMdRNrNFObF843bijhS0RKQUAO0d3t4d3vlYIctxKjl3vumfilZ1Yd758vhD0gMin6Xf7jdbhobG1m3bh2///3vaWxsxOVyhSM2IYQQOsprO2jvcgcdP1TWytdvncbKebm9x9q63FTUdQY9Z//JJm6/fNxAhimEOM1buT/omK/mKJrHhWK2hjEiMRj6nam+7bbbWLp0KTNnzmTs2LHccsstfOYznwlHbEIIIU7z+lRcbn93RKMx9GZE4+nNij5Vxeny9jkWzMcbHFVNw+ny0s/KQCHE+TAYg48piv9/Ytjrd001gKqqGE63uG1paSEuLm7QAztfsqZaCDESNbY5efHDk+w8Wo/Xp5GTGsWqeTk8s/YEjW09uud8+ooCqhs62XKoDpfbR0q8gxVzstm8v4ZjVW2656ycl4OqaWzYW0On00NclJXLZ2Zy9Zzs3oohQogL07PlaTz739EdM2ZPw3H118MckbhQF1Sn+o9//CP3338/P/7xj1F0/qA+/PDDAxvlRZKkWggx0rR3u/nR37bT3N53yZ0CXDk7i/e2B246HJsRTY/bR1VD4PrNZTMy2LS/FpfH1+d4SryD+Cgrh8tbAs65bHoG91wl1QmEuBhqdyvdr/wYrbOp74DFjmPVQxgTsoYmMHHeLqhO9a9//WsmT55MbGysblIthBBicK3dWRWQUANowO7jjXznziJWb62k5FQbDpuZ+YUpREdY+LtOVRCALQdr+dbtRazZVcWh0mbMJiPFE5MZlxHD/7ykv+bzwz3VXDMnm8RY+0C+NCFGFYMjFscN38O95y28pTtA9WHMmoK1aCWG2LShDk8MkKBJ9Te+8Q1+9KMfAXDnnXdy4403Ehmpn5kLIYQYePtPNgcdq29xEh1h5au3TO1z/HevHgh6jtPlw+ny8oVVhX2Ov7CuJOg5mgYHSpu5bHrGOUYthNBjcMRim38nzL9zqEMRgyToRsX77ruPt99+mx/96Efs3buXK664gh/+8IccP348nPGJEUzTNDR3N5rqHepQhLgkneuGxO4eL16f2udYf+dobiea131+53hdaF6p/iSEEHr6LalXXFxMcXExra2tvPrqq3z3u98lMjKSJ5544qKe+D//8z9paWnh5z//OYcPH+bhhx+ms7OTWbNm8R//8R+YTP2GJoYx96G1uPetRmuvB5MV87h5WGd/CsUaMdShCXHJmFmQxIkgGwtzUqI4UdXGfz+/l5qmbswmA7MnJDMhO44tQToqRkdYyKOSrpd/i9pwEhQDppwiZuSv4vUgMZiMCpNjO+l+8xf4qg8DYMyYiLX4ZozJYwbiZQohxIjQb0m9j1ksFhwOBxEREbS0BG5mOR9btmzh5ZfPFDv/9re/zfe+9z3eeecdNE3jueeeu6jri0uba9druDY+4U+oAbwuPIfX0f3GL9B80lhIiI8tKUonOzlw2Z3JaGBcVgx/fOMQNU3dAHi8KpsO1PLmljIm5QZWaFIUuGWKGc97/+1PqAE0FW/ZLhI2/4rFk+J1Y7i2KBbT+4/iqz6EfzW3hq/6EN2v/ye+hrIBeqVCCDH89ZtU79y5kwcffJClS5eyefNm/u3f/o3XXw82p9G/1tZWHnvsMb70pS8BUF1dTU9PD0VFRQDcdNNNrF69+oKvLy5tmtuJe8+bumNqUznek9vDHJEQly6bxcR37pzBqvm5JMfZiY6wMHtiMt/99HS2HtKfja5v7WFyfgK3Lh1LRmIE0REWpo5J4Fu3FTG96Q3/IumzaD0d3JZwiHtXTCA3NYpoh5mCrFi+fMNkrtA2gE+n0YzPjXv3awP9koUQYtgKusbiD3/4Ay+99BJOp5NPfepTvPHGGyQlJV30E37/+9/n61//OjU1NQDU19f3uW5SUhJ1dfpvFmL489UehRBrMr2V+zGPmx/GiIS4tDlsJm5cnM+Ni/N7j5VUt9HRHfxbnUNlzXzj1iKunpPde0ztbqWrKXgrcrVqH4tvu5PF09L7HO/YFLwTnLdy37m8BCGEGBWCJtUbN27k61//OsuXL8doDNEJ6Dw8//zzpKWlMW/ePF566SUA3a5dF1LCL1jNQHFp6e6Ixhli3Oawk5QUFbZ4hBiO2np8Iccj7BaSkqLQfB5UtwuDLQLVCYGVq88wWf3n+Hz+LowOmxmDQaHTZA66LEsxmuV+FUKI04Im1Re7EVHPW2+9RUNDA9dffz1tbW10d3ejKAqNjY29j2loaCA5Ofm8ry3NX4YHzZGFYotC6+nQHfelT6WhQX9MCOEXZTWQFGujoVW/o2Jhhp3KF36N58RH4HOjRCdjmXo1xrTx+Gr0a1j70ov47bO72LC3hm6Xl5gIC8tmZLA8ewbe4xt1zzHmzpT7VQgxqoRq/nLOGxUHwl//+lfeeOMNXn31Vb761a+ybNkyHnnkEaxWKzt37gTglVdeYfHixeEMS4SRYjRjnXs7/p5wfRmzizBmTQ08SQjRh0FRuOPyAt1SeOMzo5lS8hc8R9f3roXW2utxbXwCJTYdzLbA68Wm8+eT6byzrZJul7/EZVuXm5c3lPJ652QUe0zAOYo9BuuM6wf4lQkhxPB1SdSte/TRR3n44Yfp6upi0qRJ3HPPPUMdkhhE5oIFKJHxuPetRm0sR7FHYS5YhLlwGYoS1s95QgxbReMSeeDTM1i9tYITp9qItJmZNzmVJdHlqBv11057T2zBsfI7uA+txVd9EIwWzPnFlMXNZfeLx3TPeedgN0vv+Q5RpR/gLd8NgClnOpZpKzBEJgza6xNCiOFG0fQWNQ9DsvxDCCHA+f7jeE9uCzpuX/FNTFlT+hx7YV0Jb31UHvScu68az1LpqCiEEJfO8g8hhBCDzNjPF5Cnx7t6PLjc/g2Ppn46N5r66bgohBDiEln+IYQQYmCY8mbhPb5Zd0yxx3CgM55X/7qd8roOFAWm5iewaGq67uPB32hm2rjEwQpXCCFGDJmpFkKIEcSUU4Qxe1rggKJQk3ct//PyQcrr/BU7NA32ljTxt9VHmDcpRfd6NyzKI9phGcyQhRBiRJA11UIIMcJoqhfPwbV4jm1E6+nAmJiLeerV/OitNqoaOnXPuXxGBrlp0azbU01TWw9pCREsn5XJ9HEX3/RLCCFGilBrqiWpFkKIUaCt08XXf7sp6HhqvIOffWFuGCMSQojhRzYqCiHEKGc0hv5zb+pnXAghRGjyV1QIIUaBSLuZCdmxQcdnTZBlHkIIcTEkqRZCiFHitmXjsFmMAcczEiNYPjNzCCISQoiRQ9ZUCyHEKFLT1MU72yo4WNqCxWygeEIyVxZn4bCZhzo0IYS45MlGRSGEEEIIIS6SbFQUQ0pzd6M624c6DCGEEEKIQSMdFcWg8TVV4tr6LL6qg4CGISEb66wbMeVMH+rQhBBCCCEGlMxUi0GhttfT/foj+KoOAP5lOWpTBc53f4O3fPfQBieEEEIIMcAkqRaDwr33bXB3Bw5oGq4dr4Q9HiGEEEKIwSRJtRgU3uqDQcfUpnLUno4wRiOEEEIIMbgkqRaDQjGGKs+loBhMqKpGS4cLl9sXtriEGGk0TUXtbkVzO8/rvLYuN909nuDX9fSgdrWgaerFhiiEEKOCbFQUg8KUX4x7Z7XumDFzMu/sbuC9HZW0dLgwGQ0UT0jitmXjiI6whDlSIYYv96EPcO99C62jARQjprwZWOfejiEyIeg5O4828OrGk1Q1dKEAk/LiuW3pWDKT/SWi1K4WXFuexlu2E1QfSmQClmnXYCm8PEyvSgghhiepUy0Ghebqovu1n6G2nJVYWxxsSb+LZ3b1BJyTkRjB9++dhdkU2PFNCNGXe987uD56OuC4EpVIxE3/gWKNCBjbebSex18+wNl/KSNsJr53bzFJDuh66Qdo7fUB51pmfwpr0cqBCl8IIYYlqVMtwk6xRuC47iEsM2/EEJeBEp2CedIylJX/zkv73LrnVDd2sfVQ4Ju5EKIvzevGvft1/bGORjxH1+uOvbKhNCChBujq8fLutgo8xzbqJtQA7j1vonldFxqyEEKMeLL8QwwaxRqBdeb1WGde33tsz4lG3N7gazQPlTWzcGpaOMITYthSmyrQXJ1Bx71VB7FMXdHnWGuni+rGrqDnHCxrwasG32CMuxtffSmm9AnnHa8QQowGMlMtBl2n00Nrp3+Gy2wK/Sv38bjT5aWlw4U6MlYnCTGwQm4EBsXk35vQ4/bS3N6DqmqYjAaUEOdYTAYU07ldVwghRCCZqRaDpry2g+c+OMHh8hYAMpIiWDU/l+gIC+1d+ktACrJi+e1L+9lzvBFV00iMsXHNvBwuK8oIZ+hCXNIMCdkoMSlobXW64+706Tzx2kF2Hq3H69OIi7JyZXEWE3Lieu/Hs82akIwptRjvye2640pUEoak3IF6CUIIMeLITLUYFHXN3fzi6V193sCrG7r4/asHmV+YikEJnDObPi6Rl9aXsOtYQ+8MdWNbD0+sPsp72yvDFrsQlzpFUbAtuBuMgfMiSnohv9hsZOuhOrw+/33U0uHi2bUnSIq147AGnpOVHMnymZmYcmdhzJ4W+IQGE7YFn0ZR5C1DCCGCkeofYlD8ffURPtxzSncsKzmSu68az7vbK6mo6yDKYWbhlDQ6ut28tL5U95woh5lHv7yg3+UjQowmvsZy3PtW46s/iWJ1YB43ny3uAv7+bonu461mIw98egbr953iUGkzZpOR4onJLJ+Zif10sq2pXjyH1+E5thnN1YUxOQ/LlKsxyiy1EEKErP4hyz/EoDhcpv8VM0BlfSfJcXa+fMPkPscffWZ30HM6uj1U1neSnx49YDEKMdwZE3OwL/tin2OHXjkQ9PEuj4/2bjd3Xzk+6GMUgwlL4XIshcsHLE4hhBgNZNpPDIpQM8qKAmajAZ+q0tTWg9Pl9Z9jDP3raJFZaiH61d998vG42tWC1hO8gogQQojzIzPVYlAUT0imeqP+Uo7JefF8sLua97ZX0tblxmRUmDk+mcL8BPaWNOmek5bg6O34JoQIbtaEZDYdqNUdi4uykuM+Rtdzr6K2+pdnGdMnYp17O8bEnHCGKYQQI45M/YlBsXxWFplJgUlwhM1EbKSVF9aV0Ha6AojXp7H1UB1rd1YyMScu4ByzycBdVxQMesxCjARTxyQwc3xSwHGjQeG2KQqetf/Xm1AD+E4dpvuNn6MGafoihBDi3MhGRTFonC4va3ZWsf1IPV6fyqTceBZOTeORf+wM2gDmnqvH4/WqbDlYR7fLy9j0aK6anS2z1EKcB1XV2LDvFJv219Lh9JCbGsWVxVkkb/w5aov+BmLzpGXYFt4T5kiFEGJ4CbVRUZJqEVZ7TjTymxf2BR2fOymFL1xXGMaIhBgd1O5Wup78WtBxJSaVyNt+Hr6AhBBiGAqVVMvyDxFW/W2ikpJ5QgwOxWiGED0VpVuiEEJcHMlgRFgVZMUSExH8zXv2xJQwRiPE6KFYIzBmTAo6bsqfHcZohBBi5JGkWoSVyWjgrisLMBoCZ8zmTEphUm7gRkUhxMCwzrsdrBEBxw2JOVgKLx+CiIQQYuSQNdViSJTWtJ/pqGg3s2BqGgumpOm2LxdCDBy1owH3/nfxVR0EkxlT/mwsk5ahWOxDHZoQQlzyZKOiEEIIIYQQF0k2KgohxCjU1eOhvtWJ16dfwvJCqJpGY6uT9tN15oUQ56elw0Vjm3OowxCDQDoqCiHECNPc3sPT7x9n9/FGVE0j2mHmiuIsrpmbg3IRS6w27qvh9c2lNLT2ADAhO5Y7lheQJXXkhejXiao2nl17nJJT7QCkJ0Zw46J83WZNYniS5R9CCDGC9Li9/PCv26lvCZwJWzkvh5uXjLmg627cV8Nf3joccDzCZuIHny0mMUbWZAsRTFV9Jz/5xw7cnr7fGinAv90ylaKxiUMTmDhvsvxDCCFGiS0H63QTaoD3dlTS3eM572uqmsZrm0p1x7p6vLy/o+q8rynEaPLW1vKAhBpAA14Pcm+J4UeSaiGEGEGOlLcEHXN7VE5Ut5/3NRvbemhs6wk6fjjEcwohQt8jpTUd9Li9YYxGDBZJqsWg6eh2U9fSHXSTlMerUtfSTacz+MxZc3sPDa2yoUOIYDTVh9pWh+r0J8sWc+g/61azAU3TqG910trpCvo4tacDta0OTfX22wnVajaef+BCjCKh7hGjQcFokHRsJJCNimLANbQ6+ed7x9h3sglNg+gIC1fNzmLFnBzA/1XyG5vLeH9HFZ1ODwZFYfq4RO68ooC4KCsAxypbeXbtCUpr/IlCWoKDGxflM2tC8pC9LiEuNe597+De9zZadysoCsbMKRTnXM+m/fqPj4+20tjew19//xH1pz+sjs2M4Y7Lx5GXFg2A2t5Az+Yn8VXuA01DsUfjmHo1BVmpHKts1b3u7IlyXwoRyuyJybyxuVx3bHpBEuZ+PriK4UE2KooB1d3j4Qd/2UZTe+AM2A0L87huYR7PrT3B6m0VAeMpcXZ+eN9s6luc/PSJHbi9Ohs6bp5K0TjZ0CGEa/cbuLe/EDjgiOOfEZ9h69HmPodNRoXLZ2byzrbKgFNsFiPfv7eYlAiNrhe/j9bZFPCYuoKb+e/dMXS7+n5NPTYzhm/dVoRFZquFCKq7x8MjT+2iuqGrz/GYCAsP3jWD5DjHEEUmzleojYoyUy0G1IZ9NboJNcA72yuYPzmV93fqb2qqa3Gy9VAdR8pbAhJq8G/oeG1TqSTVYtTTvC7ce9/SH+xu4Z7CaiaPncGm/TV0OD3kpkaxfGYmv3/tkO4pPW4f726r4Pb0Mt2EGiCl9C2+f/cjvL+nniPlLVjNRmZPSmHJtHRJqIXoh8Nm5qG7ZvLB7mp2Hq3H69OYkp/A8lmZxEZahzo8MUAkqRYDKtQmKafLx0eH60I2ojhc3sKRiuDXKKv1b+iwWeRXV4xeamMFuLuDjms1h1lwzUoWTEnrPdba6aK2Ofg5hyta8RJYMq+Xx0m8u5Y7l0+4oJiFGO3sVhPXzM3hmrk5Qx2KGCSyiEcMKKsl9IxVhDV0Mmw1G2RDhxD9MYee2VJM/vGObje1zd14vCpmk4FQbV+sZkPveUGv28/zCiH6p3Y2obbXM0JW34pPkOk+MaBmT0xh2+F63bGUODuLpqXx+uYyWjv1WxzPmZhCdISVNzaX6Y7PkA0dQmBMyMYQm47aekp3vCd9Bn98YR97SxrRNIi0m1k+K5PCvHgOlDbrnjNnYgqm1Dl4T27THVdiUjAk5g7USxBi1PHWHMX10TOoDf661EpMKtbimzDnzx7iyMRAkexEDKiicYm6LVfNJgN3XTUek9HI3VeNx2gInDObPzmVibnxXD07i8ykwE0AMZEWbr7swrrBCTHSWBfeDUZLwHElq4j/3Kix54Q/oQbodHp4ZUMpcVFWIu3mgHPy0qJZNiMTU850THmzAp/MaMa24J6LanEuxGjma6rA+dYvexNqAK2tlp73/w9v2e4hjEwMJKn+IQacqmpsPlDL5gM1dDq95KdHc0VxFhmJEb2Pqajr4L0dlZTXdhIdYWbhlDTmTErpfdN2urys21PNzqMNeH2qf0PHzExiZEOHEL18rafw7H8XX10JijUC87j5bOjK4cn3SnQfbzEZePgzs9h8oJZDpc2YzQaKJ6SwpCi9d9mVpqp4j2/Cc3wzWk8nxuR8zFOuxBiXEc6XJsSI4lzzO7wlH+mOGRJzibjph2GNR1y4UNU/JKkWQogR5PFXDrDjiP4SLICvfWoaU8ckhDEiIUTnk1/z15MPIvKzv0Mx28IXkLhgoZJqWf4hhBAjiK2f8nY2ixFV06ht7qa5PXjrcSHEAAq1CdhgBINscRsJ5KcohBAjyOxJyWzcX6M7lhBto76lmz+9cYjGNn9CnZ8ezR3LxzEmPSacYQoxqpjHzsG96zXdMVPuTBSjpGMjgcxUCyHECFKYG8+8wpSA4yajgVnjk/jLW0d6E2qAk6faefSZPdQ0dQWcI4QYGJapV2NIyAo4rjhisc7+1BBEJAaDrKkWQogRRtM0th6q83dU7PaQmxbF5TMzefyVg9QFaQCzeFo6966Qxi5CDBbN7cRzZB2e0p3g82LKmoK5cDkGh3xLNJxIm3IhhBhFFEVhbmEqcwtTe4+1dLiCJtQAR0N0MhVCXDzFYscydQWWqSuGOhQxSGT5hxh0ja1Oapq6UEfGlyJCDBtqdxu+5mo0r9vfMTFEmWmbReZYhBDiYshfUTFojle18vT7xymr7QD8m6RuWJTHgilpQxyZECOb2tFAz8Z/4KvaD5oG1ggshcuZnJfH/pNBOipOClyHLYQQ4tzJTLUYFNUNnfzy2T29CTVAU3sPf37zMB8dqh3CyIQY2TS3k+7Xf46vch+9LRVdXbh3vcpNiceJdgR2VByTEc3SGdLcRQghLobMVItBsXpbBW6Pqjv2+qYy5k5K1R0TQlwcz7FNaJ1NumPxpe/y/Tv/kw8OtnCwtBmLyUDxxBQWTU3D0k99ayGEEKFJUi0GxbHK1qBjNU3dtHe5iY6whC8gIUYJX+3REIMeoruruHnJVG5eMiZ8QQkhxCggyz/EoLCag39eMygKFrMBl8dHVX0nbZ2uMEYmxMjWb6tjiw1NVfG1VKO2N4QnKCGEGAVkploMirmFKbywrlN3bNrYBN7cUs7aXdU4XV4UBabkJ3DXlQUkxtjDHKkQI4tpzFw8RzfojilRifhaTtGz5ndoXf4Ni4akPGzz7sSYOi6cYQohxIgjM9ViUFw+I5Mx6dEBx2MiLdgsJt7cUo7T5QX8e6n2lTTxi3/upsftDXeoQowopsxCTAULAweMZkx5s3Bv+FtvQg2gNpTS/daj+FpOhTFKIYQYeaSjohg0bo+PDftq2H6kHq9PZVJuPLMnJPOjv2/H69P/Wd191XiWTpcqBEJcDE3T8J7cjufYRrSeDoyJuZgLL8f57v+gtdfpnmMevxjbkvvCHKkQQgwv0lFRDAmL2cjlMzO5fGZm77E9xxuDJtTg3+AoSbUQF0dRFMxjZmMeM7v3mNrVEjShBvCG2uAohBCiX7L8Q1wUt8dHRV0Hze09QR9T19xNVYP/mwSbJXTZro/Hm9p6qKzvxOPVL8vX4/ZSUdchmxyFOEeKyUKolor9bnAUQggRksxUiwuiahqvbihlzc4qul1eFGBibhx3XzWelDgHAEcrWnh6zXEq6vwbFuOirKxakEtclJWWDv1keEx6ND9/aldvSb5Iu5mrZmdxzdwcFEXB61N5YV0JH+49hcvtQ1Fgan4Cd181nvhoSQqECEaxRmDMmoqvYq/uuHns3DBHJIQQI4usqRYX5LkPTrB6a0XA8bgoKz/63Gxa2l385IkduHVmmq+enc37O6vw+vqOzStM4UBpMx3dnoBzbliUx3UL8vjrW4fZsK8mYDwlzs6PPjcbs0kaWAgRjNpWS/drj6A52/ocN6YWYL/mW/7ZbCGEEEHJmmoxoLp6PKzdWaU71tLhYuO+GirrO3UTaoA9Jxr5wWeLWbOzioq6DqLsZhZOTaOyvlM3oQZ4d1slxROS2bRfv8V5XYuTrYfqWTg17cJelBCjgCEmFcfN/4Hn0Fq8VQdRTBZM+cWYCxZKQi2EEBdJkmpx3spqO4ImzODfbFhZr1+jGqC2uZsou5l7rhrf5/jaXdVBz+l2edlxtB41xBcrx6paJakWoh8GRyzWWTdhnXXTUIcihBAjimxUFOetv82GdqsJu7X/joqapwdfYzlqZ/M5XTfKHnomzW6Rz4hCCCGEGBqShYjzlp8WTXKcnfoWp+743EkpZCRGBJ2tLhqbALtepPPwB+DpARSMGZOYk3cju4/rP2d2SiQLp6bxysZS2rvc+s9bmHIhL0cIIYQQ4qLJTLU4b4qicO/VE7CYA399FkxOpTAvnmUzMxmbGRMwHhdl5YaYA3j2vX06oQbQ8FUfZMLh/2NmQULAOXarkbuvGo/JaODeqydgMgaWBbtiVhZ5aYEdHIUQQgghwkGqf4gLVtfczZqdVZTWtBNpN7NgShozxyehnK6F6/H62LS/lu1H6vH4VApz41kyIQrjKw+Aqt+O3LzgHvYwiS0H6+h2eRiTHsPymZkkxtp7H1PV0MmanVVU1XcSHWFh0bR0isYmhuU1CyGEEGL0ClX9Q5JqEVbe8t043/l10HHTmDnYL/+XMEYkhBBCCHFuQiXVsvxDhJfZHnJYsYQeF0IIIYS4FElSLcLKmFqAEhm4bvpjprHzwhiNEEIIIcTAkKRahJViMGBb/FkwmgPGzJOWYUobr3OWEEIIIcSlTdZUiyGhttbgPvg+amMFij0Kc8EiTLnThzosIYQQQoigZKOiEEIIIYQQF0k2KopB43R5Ka1pp75VvxEMQHVDJ2W17Xh9wVubn62uuZvSmnZcHt9AhCmE+ASvT6Wstp3qxq6gj1Hb6/HVn0TrrScvhBAiFOmoKC6Iqmq8+GEJa3dX43L7E9+CzBg+s2ICaQkRABwqa+af7x/n1Ok37ugIC9cvyGXpjMyg162o6+CJd45y8lQ74G95fsWsTK5fmNdb/1oIceHW7Kzi9c1lvZ1JMxIj+PQVBUzIiQPA11KNa/3f8NWdbm9qtmEpXI6l+CYUReZhhBAiGFn+IS7IM2uO8+72yoDjMREWfvz5OTS39/CTJ3bqzk7fd81EFk5NCzje0uHi+3/eSldPYGOY6xbkcsOi/IEJXohRav3eU/zt7SMBx80mA9+7Zxbp0Rrdz/87mrM98DFTV2Cbe1s4whRCiEuWLP8QA6rT6eGD3dW6Y21dbjbuq+GdbZVBl3u8uaUMvc9yH+6p1k2oAd7bUdU7Iy6EOH+qpvHmljLdMY9X5Z3tFXiOfKibUAN4Dq1BcwVfLiKEEKOdJNXivJXXduDxBl8ffbyqleNVrUHH61qcdHR7dM5rC3qO0+WlqrHzvOIUQpzR1ummoTX4+ugTVW34ao8Hv4DXja+pYhAiE0KIkUGSanHeHLbQS/EdVlPIxxgNClazke4eLyWn2no3OTqs/V9XiJHI5fFx8lQ7NU0DOxNc3+qk5FQb3T1erGYjhhD7EuxWE4rFEfJ6/Y1/rKapi5JTbUG/XdJcXfjqS1A7Gs7pekIMN22dLkqq22jtdA11KCKMJEsR5y03NYrUeAe1zd2643Mnp5JR10lF3Qnd8WljE3lxfQnr957C7fHPeI/PimXWhGR2HtN/k81NjerdACnESKFpGq9vKuOd7ZU4Xf6lTzmpUdxz1Xjy0qIv+Lo1TV38/e0jHDv97Y/FbOCyogymjolnz4km3XPmFaZiTpmH98QW3XFDXAbGxJyQz1te28ET7xyhtKYDALvVyPKZWdywyL/RWPN5cG15Gs/RDeDzf1tlzJiEbfFnMUQlXejLFeKS0d3j4e+rj7LzaAOqpmFQFGYUJHLP1ROItAc2PRMji8xUi/OmKAr3rpiA1WIMGFs8LZ3C3Hgun5nB+KzYgPGEaCtmk8L7O6p6E2qAo5WtvLmljFnjA99YI2wm7rlaOi2Kkef1zWW8srG0N6EGf2L6y2f20NR2YaXsuns8/NfTu3sTagC3R+Xd7ZXYrSbioqwB50zMieOy6RmYsqdiHr8o8KJmu78TaggtHS4efWZ3b0IN4HT5/K9xQykAPev/iufQ2t6EGsBXfYjuN36B5pUZPTH8/eaFfWw/Uo96et+QqmnsONrAr5/fO8SRiXCQmWpxQQqyYvnxfbNZu7ua0lPtRDrMLJicRtG4RADMJiPfvL2Ijw7Wsf1IPR6vj8K8eIrGJfLDv2zXvWZrp5uCrFhmT0xhy8Faunu8jM2MYen0DOKjbeF8eUIMOrfHx3s6FXQAul1e1u6u4lOXjT3v627cX0trp1t3bPuRev7js7PZdbyBg6XNWMxGiickM2dSCiajf47Fuvg+jDnT8R7bhObqxJg8BvOkZRiiEkM+77rdwTcav7+zkqsnRwSdBdc6GvCc+AjLhCXn8UqFuLQcq2zt82H2k0pOtXO4rJmJufFhjkqEkyTV4oIlxtq5dWnwN32T0cDCqWl9yuftPt6AL0TpwxPVbXzp+snMmpA8oLEKcampaeoOmoQClITYuBvKiRCbhL0+jYY2Jyvn5bJyXq7uYxRFwZw7A3PujPN73upQG419NJ08REyICq6+2hMgSbUYxkJt0Ac4Xt0mSfUIJ8s/xEXp7vFyorot6PpqTdOoqOug5FQbHq/a72bDCJt/zZmv5RS+uhNBu7lpPZ346k6gttdf3AsQYojY+9vwe/peUNvr/fdCj371G6fLfw9+vMmx343ENjMer0rJqTYq64NX1Klt7uZEdVufpSmf1NXj4UR1G/Ut/nu/v3vbYtev6/oxxXpumyCFuFR9fM8GE2Ezo2ka5bUdnDx1fl2GxfAwJDPVv/3tb3n77bcBWLJkCd/5znfYvHkzjzzyCC6XixUrVvD1r399KEIT58inqjz/QQnr9lT3ro0ekxHNvSsmkpHo31B4oLSJp947Tt3phDvKYebaeTkkxthoDLJedHamRtdLP0RtLPMfMNuwTL4Cy6wbURSDf6PT5qfwHNsIPv+bvTF9IrbF92GIlo1OYvhIjrUzNiMm6AzvnHw73a8/gq/mqP+A0Yx5/CKs8+5AMZpRNY2X15/k/Z1narjnpUWxpCiD9Xtr9J8zzk5JdRu/eWEfnU7/uua0BAefvqKASadn0KrqO/nb6iO9XU2tZiNLZ2Rwy5IxGAwKXp/Ks2tPsGHvKdynS2sWZMYwe1JKyI3GSRNn0LU7Fq27Vfcx5nHz+/9HE+ISVjwhmWfWHNctOWsyKtitJh78/Ue9Fa+iIyzcsCiPy4oywh2qGCRhn6nevHkzGzdu5OWXX+aVV17h4MGDvPHGGzz00EM8/vjjvPXWWxw4cIAPP/ww3KGJ8/DsmhO8u72yz2bDkup2/uvp3XR0uymv7eA3L+zrTagBOro9PL3mBLPGJ2ExB/7qLS9KIn3H/5xJqAE8Pbh3v457x8sA9Hz4FzyH1/Um1AC+U4fpfvM/ZaOTGHbuvmq8bkWA4vEJTDz8uzMJNYDPg+fQWno2/B2Alz48yZtbyvuUrSut6eCFdSUsnBLYsdRqNjJ9bCLPrj3Rm1CDfxnKr1/YR0VdB+1dbv7rmd29CTX4y/2t3lrB8+v81XyefPcYa3ZW9SbUAMeq2nhjcxnFOsu2ImwmPnP1BBSDEduS+8AY+HotRdf2W1lEiEtdpN3Mp68o4OzKlQpwxaws/vrW4d6EGqC9y80Tq4/y0cHa8AYqBk3YZ6qTkpJ44IEHsFgsAIwZM4aysjJycnLIysoCYNWqVaxevZolS2R93aWo0+lh3Z5TumPtpzsqVjZ04vXpr5/cdbyRH903mw92V1Ne20F0hIWFU9IY17QOd4V+nV73gfcxjZmDt+Qj3XGto1E2OolhJys5kh99bjbrdldzrLIVu9XEvMJUJvsO4NmkX/rOe3wz3VOvY82uKt3xTqeH+GgrX/vUNDbtr6Gj201uWjRLitL55TN7dM/xeP3VQVLjHbqNmQA+2FXNomnpbNqvPwve2ulmbEY0syem8NHBWrp6PKc3Gmf2VhwxZU0l4pYf4z64BrW5CsUeg3nCYkwZk/r5lxJieFg8LZ2clCg+2F1NfUs3SbF2LpuewVtbyoPuJ3pzSzlzC1PDHKkYDGFPqseNG9f732VlZbz11lvcfffdJCWd+eo+OTmZurq6cIcmzlF5bUfItWAnqkOv1axvcWKzmLht2bg+x7sP6de1BsDjxFu2C2SjkxhhYiOt3LAov88x5wch7gVNpbHkcNDGKuCvNHDDonymjknoPdbS4Qq67AqgpLqtzwz22dxelZ1HQm80LjnVzhXF2czUKY35MUNMKrb5nw46LsRwl5Maxb0rJvQ5Fmojb3VjF06XF7s0OBv2huwnePz4cb74xS/y3e9+F5PJRGlpaZ9xJUTnLz0JCaE3wYiBkxGiYgFAfKyddqcn6Bu4yaiQkRaDWXXiaazC4IjBkpBOXXQMXfoT4ABEp6TQGOJ5I+JiSUiKOodXIMSlrTE2lvYQ4wmpiUDwiYe4aBtJSVFUN3TS2uEiJzWKiCgbRoMSNCmOjrQSH2sPGVdWPw1pEuIcJMk9KESA6EgLbV36pS4tJgPpaTG9ZS3F8DUkSfXOnTv56le/ykMPPcTKlSvZtm0bjY1n0qX6+nqSk8+vpFpTUydqiBkUMXBibSbSEhzUNOlX/Jg+JoGkaFvQkmDTxybS8Nbv+3RVM6SMxTx2HqC/vMOQlEdP2iwUR/CNTp6MWTQ0dOiOCTGc+DKLYftbumNKRDyOjInkpHRTXqf/+56bEsnXfvUBJdX+1NxiMrBoWjpTxySw+7j+R9Pi8UmkJjhYt1N/WUlmUgRT8+JCbjSePiZB7kEhdMyekExlnf43uMUTkmlp1l/6KC49BoMSdCI37B+Lampq+MpXvsKjjz7KypUrAZg2bRqlpaWUl5fj8/l44403WLx4cbhDE+fhs9dMxG4N7Ki4dHoGk3LjWTo9g4k5cQHjiTE2ro/cFdBVTa07gWvXa5jGzA58MmsEtkX3+jc6XfZ5MFoCHmKZvko2OokRw5iUh2XaNYEDJgu2yz6PYjDwmRXjidApnzd3UgqvbSztTajBv3Rjzc4qrBYjCdGBHRULc/0dFSfnJbB4WnrAuN1q4t4VEzEoCvddMxGrOfDev7I4i7EZMef5SoUYHZbPymJcZuD9kRJn5+bLxgxBRGIwKJoWYpHqIPjJT37Ciy++SHZ2du+x22+/ndzc3N6SekuWLOHBBx88ryUgMlMdfk1tPazbU83JU+1EOczMn5zWZw2n16ey/XD9JzoqJrBgrB1eegA0/fWglnl3YohKxHt8M5q7G2PKWMwTl2KIOJOgq211uA+tRW2uPLPRKX3ioL9eIcLNW30Iz9H1aM52DAnZWCYtwxB95lu8lg4XH+yupqS6jQi7mfmFqdQ2d/HcByW61zMZFf7jvjnsOdHAodJmzCYjxROTKZ6Q3Oer570nGtl8oJZOp4f89OiArqYNrc6AjcaFedLUQohQPF6VrYfq2Hm0Hq+qMSU/gUVT02Qt9TATaqY67En1YJGkenjwlu3G+e6vg46bxszBfvm/hDEiIUaWx1/ez46j+vWiAb72qalMHRO65bgQQgh9oZJq+XgkwqufrmmKNSJMgQgxMvXX1c1hM+P2+Civ68BsMpCTEnXeG8OFEEIEkqRahJUxdRxKVBJah/5MmnRVE+LizJ+cyvq9+mV0UuLsHK9s5dfP76XrdBWf5Dg7d11RwOT8BN1zhBBCnBup3yLCSlEM/s2GZlvAmHnq1RhTxg5BVEKMHAVZsVwxKyvguM1iZNrYRJ5fV9KbUIO/bvxvXvR3VBRCCHHhZE21GBJqRyOeQ2vxNVWg2KIwj18kXdWEGEAHS5v9HRWdHnJTo1hclM4vntpNU7t+Obx5hSncv6owzFEKIcTwImuqxSXHEJWIdc6tQx2GECNWYV58n4ocLR2uoAk1wMlTodrNCCGE6I8k1WLQVNV30un0kJUSSYTO5im1pwO1uQrFHoMxLrA2rqpplNa04/Np5KVFYzbJaiUhzkd1YxcdXW4ykyOxWYwhOyp+vMGxvtVJc1sPKfEO4qICa1oLIS6cvK+NbLL8Qwy48toO/vr2YSpOd4+ymAwsLkrntmVjMRoMaD4Prk1P4Tm2EVT/2k5Dcj62JZ/DGJcBwM6jDTyz5njvzFqk3cx1C3JZrrNWVAjRV1VDJ3996zClNf510iajgUVT02jrcrHrmH5HxRsX53OsooWDZS0AGBSFGeOTuPfqCTh0mswIIc6PvK+NDFKnWoRNW5eb7/1pK51OT8DY8pmZ3HlFAc51f8Z7bEPAuOKIJeJTP+VEg4f/fGo3qs6v5udWTmTBlLRBiV2IkaDT6eHhP35Ee3fgPTh7YgonT7UFtBmfnB9PY6uT2mZnwDmTcuP41u3TBy1eIUaD41Wt8r42QlxSbcrFyPbhnmrdhBrgw72naG9qwHt8k+641t2K59gmVm+t0P3DA/DWR+UDFqsQI9HGfTW6CTXAzqP1fOPWady+bCxT8hOYWZDEl64vZOHkNN2EGuBQWQulNbLeWoiLIe9ro4N8pycGVKjNTh6vSuWJUrI1NehjfPUllJwK3iCmpqkbp8srbV2FCOLkqbagYz5Vo77VyZWzs7lydnbv8efWngh5zdKadvLSogcsRiFGm5IQ743yvjZyyEy1GFB6GxL7jEf131Ex0h78GhaTQTZ2CBFCRIj7B/z3qMvj42hFCyWn2lA1jQh76Dfz/u5rIURo8r42OshPUQyo+VNSg45lJUeSM7EQJTo56GPMBQuYPzn4NWZPTMFklF9bIYIJdf+kxjs4XN7MN367if/8525++sROHvjdFmIjLRiCtCq3W00UjUscrHCFGBXkfW10kJ+iGFCFufEsm5ERcDzCZuK+ayaiKAq2y+7X7ahoKVqJMXkMV8zKZEJ2bMB4WoKDWy4bMxhhCzFijMuM5epPLO34mN1qYuqYBF5aX4rTdaajYmNbD39ffZSrZmdxdlptMircd81ErGbjIEctxMgm72ujg1T/EIPiYGkzmw/U0NXjJS8tmiVF6cRGnql5q3Y24Tm8Dl9jOYo9CnPBIkzpE3rHvT6VHUfq2Xm0AZ+qMTk/nvmTU7FZZM2ZEOficHkLm/bX0On0kJMSxeJp6fz8qZ00tbt0Hz+3MIWrirP5cE81Te0u0hIcXDY9g9T40Eu2hBDnRt7XRgYpqSeEEKNcS4eLb/6vfuUdgJQ4O498cV4YIxJCiOFH2pQLNE3jZE07LreP3NRo3WYOze091DR1Ex9tJS0h4pyu297tprK+k0ibmZzUqIEOO6iWDhenmrqIibCQmRT4y+31qZw81Y6qaozJiMZsCvz6uqapi+YOFylxdhJj7OEIW4gBo3ld+OpPgsGEMXkMiiFwNZ+vqQLN2YEhPhObJSJkR8WPNzjKfSGEEBdGkupR4HBZM39ffZT6Vn8dWqvZyPJZmdy0OB9FUeju8fK3tw+z81gDH39vUZAZw+evnURirP6bqten8s/3jrFxfw1en/+kzKRIPrdy4qAm1y6PjydWH2Hb4fre5CAvLZrPXzux94PAlgO1PPfBCdq63IB/1/UNi/JYNiMT8H94+NMbhzhS0QqAokDR2ETuWzlRqhyIYcG9921cu18HdzcASkQ81nl3YM4vBsDXVEnPuj+iNlX4TzAYMRcsYPq4Wew4qt9RcdrYBH7xz11yXwghxAWS5R8jXE1TF//x1+24vYG1oW9cnM+q+bn86tk9HChtDhhPjrXzk/vn6O5KfmL1EdbtORVwPNJu5iefn0N0hGVgXsBZHn/lADuO1Accj4uy8tP751BS3c6vnt2D3m/Cl64vZOb4JL73p23UNncHjE/MiePbd0jnOHFpcx/5ENf6vwYOKAYcqx7EEJtG13MPovV0BDykM3cpvz5Z0PsB+2NTxyRQ19xNXUtgAxi5L4QQ4gzpqDiKvb+jSjehBnhveyUnT7XpJtQA9a1O3QS2vcvNxv01uud0Oj2s3xuYbA+E+pZudurEA/7lIFsO1vH21nLdhBrgrS3l7D7WqJtQg39jl3SOE5cyTdNw730ryKCKe99qPEfX6ybUAJEV6/nebQXcuXwcRWMTKZ6QzJdvmMz8yam6CTXIfSGEEOdKln+McCdDvBl2Oj3sP9kU8vzSmg7mFvatr1lZ39m75EP/nMF5Ay6r7QiaMAOUnmqntEY/mQCoqO/kRHXwbnMgnePEJc7djdZWF3TY11DqX7cRjOrD2lHF8lnTWD4rq/fwcx9IR0UhhLhYklSPcKG6OClAfFRgvei+55vQNBW1rgTN68aYnB/ymp98zrLadjqdHrKTo3SXg6jdrajNVSi2KIyJOQHjPlXlRFUbPlVjTEZMv53iIh1mIu2mPjV4P8luNfa7LKW/1ybEkDJZwGgGn0d3WLFFoNj0v5Y885gonC4vJ2vasZgMjEmPOed7WgghRHCSVI9wCyancjDI8o7C/HjmFqby/LoSOp2Bb9KKAsVxLXQ9/Vu0ztMz2mYbqVOuIiMxnerGLt3r5qVF870/be0dNxkVFk5N587l4zAZDWheNz0bn8B7fAtoPgAMCdnYlt6PMd4/e7btcB1PrzlOW6d/s6HDauKaeTnER1tpDlJnd/7kVKxmI69uLNUfL0xj7qQUXvrwJKrOVgKH1UTRWOkcJy5ditGMacwcvMc26o6bxy3AmDIWz+F1uuOG2DTePK6weusmetz+ey8+2sr1C/IwKIrcF0IIcRFkTfUIN3tSCnMnpQQcj4+2cteV4zGbDHz+2omYTX1/FRTgtrmJRGx+/ExCDeDpwb3rVe4paCFCpyzfZUXpvLCupE/C7fVprNtdzdNrjgPQs+Fv/qTgdEINoDZV4Hzzv9BcXRytaOH3rx3sTagBul1eXlhXwpwJKVgtgeXxblqcT2ZSJFfPyaYgMyZgPDs5khsW5xEfbePTVxbodI4z8LlrJ2KRznHiEmedcyuG2LSA48bMyZgLl2NMGYulaGXgiRYHe5Ov45UNpb0JNUBzu4sn3jnK1XP0OirKfSGEEOdKqn+MApqmsa+kia2H6+hx+ZiQHcvCqWk4PlEmq6HVybo91dQ0dhMXZWXxtHSSjzyD95h+swjFEYv3+kfYsK+W0poOIu1mFkxJ5UhFa9CZYpPRwKP3TkR55UHQ9DdPWufezu+OpbPnhH7Zr9R4B9+6vYh1e6qpqu8iOsLComlpjEk/k0h/3LVq1+kSgVPHJDBnUkqfxKCiroP1e0/R3O4iNcHBZUXpJMdJ5zgxPGgeF57jm/BVHQCDCVP+LEy5M1EMZ37HvTVH8R7biNbTiSExF+P4xXz374do6dD/pmfOpBRWzMmW+0IIIUKQjorignQ9/xBqS/BKHhF3/BeGqKQ+x/77+b3sKwm++fEbSyPJ2ft40HHTmDk8eGgK7V3uoI/5368vxm6VlUtCnI/+Oiomx9n5uXRUFEKIkKSknrggii1EExfFiGJxoHa34q3c5+/sBkT1s6EpKjp0p0bFFkmUI/g1LGYDZpOB5vYe9p9soqIueLUPIYaz2uZu9pU0UdOkv3fhfNksRoyG4JVB+rt3z5WmaZRUt7H/ZBPt3cE/HAshxEgj030iKPO4BfhqjuqOGXOm4froWTzHN4F6erNhXDpzC+5i0wH96+WkRJE9YRJd+1LR2mr1n7NgIQtMxqAlvoonJPOnNw6x40hD76aqnJQoPr9qEhmJ59ZaXYhLWVuXmz+9fpCDZS29xybmxPH5aycRF2W94OvarSZmjk9i22H9Wu8LpgSu0z5fxypb+etbh3trXpuMCkumZXD78rEYddqoCyHESCJ/5URQpoKFmPJmBRxXopJQjGY8R9f3JtQAasspsvf8L1cUJQecE2k3c9/KiSiKgn3p/WAJXKdpmXE9xqQ8Lp+ZSWFuXMB4RlIEnd0eth2u71OloLyug0ef2R20lJ4Qw4Wmafz383v7JNTgb8Dy38/v5WJX691++ThS4uwBx6ePS2TRtItLqhtbnTz2/N4+TWS8Po01u6p4/oOSi7q2EEIMB7KmWoSkaSq+in14SraC140xYyLGzCl0P/9Qn4T6kyyzbqIscRGbD9TS6fSQnx7NomnpRDvO1IhWu1vxHPkQtbEcxRaNefxCjCljz4yrGruONbDzWANen8qU/ATy06P5wZ+3BW0Ac9eVBSybkTmQL1+IsDpY1swvn9kTdPxrn5rG1DEJF/UcPW4vWw7Wcai0GbPZQPGEZKaNTcQQqmnMOXj+gxO8vbVCd8xqNvLLryzAoVMxSAghhpNQa6rlL5wISVEMmHKKMOUU9R7zVh0ImlADqA2ljJ9xHeOzA2ebP2ZwxGKdcX3wcYPCrAnJzJpwZtZ72+G6kB0Vy0J0UxRiOCjrpxtpWW37RSfVNouJpdMzWDo946Kuc7ZQnVRdHh81TV2MyQgsdymEECOFJNWCti435bXtOGxmxqRHo5w1Y6WpKr664+B1YUweE3oDI/4NjpqmcbKmnS6nh5yUKGIiA9eCNrf3UNXQSZTDotsC2etTOV7Zik/VGJvZf9e3KIcZVdM4UdVGj9tHXloUUQ6dTo7tDait1SiOON1OjkIMFb3f17PHz74vbJbAP+PVjV00tjpJiXeQGh+41Epzd+OrOwFGC8bUcX1K8QEXdP/2F3tkiA3IQowW53L/iuFLfpqjmNen8s/3j7Nh7yl8p5fOpMTZuW/lRMZlxvofU7abnk3/QOs63ZXRZMEy+UoM8VmozZW6162PL+J3f9xKbXM3AEaDwoIpqdx15XhMRgMuj48nVh9h66Eza6MzkiK4/9pJZKf4E/aPDtbyzNoTvaX1bBYjK+flkBBto6m9R/d5U+IdPPC7LTS2+cdNRgNLp2dw27KxGAwKmrubnnV/xlu2C07PeRuS8rAv/SKG2NSL+8cUYgDMGp/M0+8fx+UJ/CbIYjKgAN96fHOf++La+blcM9f/4bC5vYc/vH6IY5WtvecV5sXz+WsnERPhT3pdO17GvW81eP31qhVHLNYFd2POmwlASXUbf37z8Hnfv5cVpbP9iP4myHGZMaRIvWsxyum9r33y/hXDn6ypHsWeeu8Ya3ZWBRy3WYz8+HNziPXU0v3Kj3WXepgKr8BX8hFaT98lF+rEq3loR7rupsHLpmdwz1Xj+cNrB/noUF3AeKTdzM++MJeq+k7+65nd6P1mrpiTzQe7q/t0hAO4anYWH+yqxu0NbCqzcl4ONy8ZQ/fbv8RXuT9gXImIJ+K2n6OYQs+0CREO24/U84fXDvZ+0AV/YrtiTjZvflSue1/cu2ICC6ek8f2/bONUY2AJvry0KL73mWLc+1bj+uiZwAsoRhzX/zvt9gwe/tPWC75/p49LZMO+mj7HYyItfOeO6aQlSHUeMXodKW8J+r5274oJLJ6WHv6gxAWRNdUiQFePhw179Ru79Lh9rNtTzTXqmqBrp30nt+G4+Ud4jm9GrT+JYovCXLCA148bcbrKdM/ZuK+GJdPS2Xo48A0ZoNPpYeO+Go5UtOj+4QHYdayBn94/l/V7T1FV30l0pIXFU9PZuK9GN6EGWLurmmsmmHQTagCtqxlvyVbM4xfpP6kQYVQ8IZnslEjW7zlFXYuT5Fg7i4vSeWbN8aD3xdsflRPlMOsm1AClNR0cLmsia/87+hfQfLj3v8OHlquCVtHZuK+GJUVpIe/ftIQIvnPHdLYcrKW7x8uYjBgWTUsjwiZLP8TotnpbRcj7V5LqkUGS6lGquqEraBIKUFbbgWooCzquOdtA9WItWtn3vA17g57j9ansO9kY9A+L/3nbKasNvuGwrsWJzWLk+oV5fY4/+a5+PW0Ap8tLe8UxQn357Gsok6RaXDJS4hx8aunYPsf6uy9KqtpCXrOssoHMrpag477GMsoMwZ/D61PZd6K53/v36jnZTMgJvklZiNGov/vX6fJKp+ARQOpUj1LREf1siLKbz6mjYkuHiz0nGjlR3YamaSG7IQIkRAfWyO3zvA4L0SGuYTUbsZgNNLY62XOisbfiQKhNUgpgjQr9Jq/Y9L/KEeJS0d99obeZ8JOiohxgCP6mrdii+r9/Y/p5jn42KwoxWp3L+5oY/uRj0SiVGu9gTEY0JdX6ZbAWTEnD7F4YtKOiIaeIv7xXzkeH6nrXfqbGO1g+K5NN+/W7JeamRjGvMIU3Npf1boI628IpaSTG2Hh2rX5HxVkTkvj9qwfZeayhd8YsMymShVNT2XOiUfecSblxxBRMoWtHLFp3a+ADFAVzwQLdc4W4VCyYkhb0vphbmMLcwhRe+LAEj843UDaLkVmT0tGaZuEt+Uj3GuaChSyMSOvn/k3ljc3lIe9fIUSg/u5f6Tg6MshPcRT77IqJvRUBPumKWVkU5sVjKliAKX92wLgSk8KrrjlsOlDbZzNVbXM3L354Urf+bbTjTEfF+1dNIkKnCcSNi/LISY3i8pmZTMkPrMWblRxJe5ebHUcb+nwFXdXQyVsfVTB/cmAFj4RoK3dfPQHFYMK27ItgOnumTcE679MYogO7QApxKQl1X9y8ZAxRDgv3rpiA0dC3JKbJqPD5aydhs5iwzrsdQ0zgfWLKm4V5/GLGZ8dx9ZzsgPHzuX+FEIH6u3/FyCDVP0a5rh7/5sCS6jYi7GbmFaZSkBXbO65pGr6q/XhKtvnrVKdPwpk+k2/9YWefhPqTbliYx6TceDYfqDndUTGGhVPT+tSZbu9ys2HfKcprO4iKsLBwSlqfWtWqqrH3RCM7jjbgU/0dFbNTovjhX4J3VPz0FQWkJ0bw0cFanG4fBZkxLJiS1medmtrV4u/k2FyFEhGHecJijPFZF/VvKES46N0XsycmYzadqTNd29zN+r2naGh1khrvYPG0dJJizyy70rwuPCc+wld1EExmzPmzMWZN7VOf/kRV20Xdv0KIQOdy/4pLX6jqH5JUi/N2sLSZXz67J+j49HGJ/NvNUwf8ebcdruN3rx4MOr5wShr3rZw44M8rhBBCCAFSUu+S5fWpHKtsxe1Rg3YMrGnqorapm4QYW29jlE9yurwcq2zFaFAYnx2r+4m35FQb7Z1uspIjSYwNvVHwXPS3mSnKYUbTNI5XtdHl9JCbFk1cVOAGp8Y2J5V1nURHWHTbF2s+j39Nt+rFmDqeqHPoqDgQXB4fRytaARifHYvVHPhvWlbbTku7i/TECFJ0Ota1d7k5eaodm8VIQVYshrO+khdiKHX1eDhe1YbZZGB8ViwmY9+VgJqm4as7jtbTiTEpD0OEVPMQQoj+SFI9RHYebeDJ947S1unvrGQ2GVg+M5NbLhuDoii0d7n5w+sHOVR2pgTWmPRovnBdYe9XuW9uKeONLeW4TjdCibSb+dRlY1h0ut5lRV0Hf3z9ENWna9cqCswsSOKz10y8qNI92SlRZCdHUlHfqTuekxLFg3/4iPoWJ+BvXDG3MIV7rpqA2WTA5fbx17cPs/1Ife/a6LQEB/evmkRuqv8rZM+xTbg+euZMcxmTldxpK0mMSeztmPhJCv6NIBdrzc4qXlp/srdWr91q4sZFeSyf5V8iUtPUxe9fO0hFXWfv804dk8DnV00iwmZGVTWeXnOcD/dU4/X5X1x8tJXPXD1Bdz2dEOGkaRovrT/Je9sre0tqxkRYuPOKAoon+PcVeGuP07PuT2jtp+tRK0ZM4+ZhW/QZFKPUmxZCiGBko+IQKKtt53evHuhNqAE8XpW3t1bw1kflAPzmxX19EmqAklPt/PLZPfhUlQ17T/Hihyd7E2rwN1/429tHOHCyia4eD798dk9vQg2gabDjaAN/eevwRb+Gz107SXdmePnMTF74sKQ3oQbwqRqb9tfy1HvHAPjr24fZdri+z2bDmqZufvXsXjqdHrzVh/xv6p/s1uh14d35EvdN6dH9QHDrsrGkJ15cx7YdR+p56r1jfZpfOF1e/vn+cXYcqcft8fHoM3t6E2rwNzvfW9LE/71yAICX1p9kzc6q3oQaoLndxf+8uD9oYw4hwmX1tgre3FLep0Z9W5eb3796kJLqNtTOZpxv/+pMQg2g+fAe24hr05NDELEQQgwfMlM9BN7dXhl0k9972yvJS4vm5Cn9Unf1LU52H2tk9bYK3XEN/xvn1PwEOro9uo/ZdbSB+pZukuNCtUMJLSs5kp/eP5eN+2oorWkn0mFmweQ09pxoxOnS78K4+UANlxWls/1Ive54p9PDhn2nWNK8+vQrCZRZ9Q4/+8KPezsqxkRYWDg1TXdpzPl6e6v+v+nHY06Xl5YOl+74obIWTlS38sHuwLbv4F/qs2ZnFXdfNf6i4xTiQvhUlXe3V+qOqZrGu9sruS/jKHicuo/xHNuEpfhmDHbZkCiEEHokqR4Cn5zpPFt7t6d3PW8wJ0+1UdOkXyf24+vHRARv0qCdfszFJNXgX25ydvmt1zaVBn2816exv7QpZEe28toO1PayoONaex3RZh+r5ueeZ7T9q6gL3vGqoq6D8hDj4N/AGewDBdDv+UIMpvYuT59vx85WUdeBz1oe/AKqF7WlWpJqIYQIQpLqIRDtMHMqyJjRoPTbtSwm0ordagyawEU5zERHhF77GB1hQXN14as5BkYTxvQJAesl/ZuVTqA52zEm5mCISgy4TlNbD2W1HUQ5zIzLjOl3s2BSTOiNktEOC4onGs2pP1OP2QYmC2p7Pb6mSgyOGIwpYwMe5vb4OFLRgqrB+KxY3SUjvoYy1M4mDLFpGOPSiY6wBJ2JjnKYie6nW1xijB2FYHPs9Hu+EAPp7PvXbo3FZFT6LE36pCiHBaWfhFmxSUIthBDBSFI9BBZMSeNIkNnoGQVJzJmUyvMflNDV4w0YNxkV5hWm0tjWw5qd+ksNFk5JY8qYBN7Zpv9Vb0qcnezaeGQaygAAKZVJREFUD+h8723w+meuFFsU1rm393YW9NWfxPnB79HaPt6spGDKn41t8X0oZituj4+/rT7C1kN1vTPPyXF2rizOCtqRLS8tirmFqby+uSzoTPuCKWmY6xfh+uhp3XFTfjE97/8v3rLdfJy+GuIysC37IsYE/6z5ut3VvPjhmX8/q8XIqvm5XDM3BwC1rQ7n2t+hNpyZVTdmFLJg4rW8sa1G93kXTk1j/uRUXttUhqoz1R4TaWHOpBS2Ha5n/8mmoK9NiHAIdv/OKljIR4cbdM9ZODUNc3Ik3mMbdccNSXkY4wMbOwkhhPCTjYpDYN7kVOYWpgQcT413cMfycVjNRu5fVYjF1PfHYzQofPaaiURHWLhxUT55aYHriCfnx3NFcRaZSZHcujRwBjfCZuLeSV14dr/am1ADaD0d9Hz4J7ynDqN2t9H91qNn3pABNA1vyVZ6NvwNgH+8c5SPDtb1WcpR3+LkxQ9LuHxGZsDzxkRYuG/lJAC+sKpQt3zgzUvyyUmNwjz5cozZ0wLGDYk5qF0teMt28cn5YLWlGudbj6K5u9lzopEn3jna5wOJy+3jhXUlbNpfg+bz0v3Wo30SagBf9UGWtr3cp/HNxwoyY1g5L5fEWDt3XVmAclZ1PKvFyJeuK8RkNHD3VQUkxtgCrnFZUTozxycFHBdioIW6f2+wbdXd0Dt7YjILp6ZhShuPpejagHHFHoPtss8PZthCCDHsSfOXIXSwrJnth+twe1Qm5MQxd1IKlk/URG5u72H93lPUNvvrVC+elk7KJ9ZBe30qO47Ws7+kCYNBYUZBEtPGJmL4RNZXUdfBxv01tHe5yU6JYuGUVAyv/TtaR6NuTMbsaRhTxuLe/qJ+0IoB3/U/49t/Oxx0s+X1C/OYkp/A5gM1dPV4yUuLZuGUVBy2M4n0x5sSK+o6ibKbWTAlrU+LY01T8VXuw3NyJ6heTFlTMMRn0f3i94L+e1rnf5r/3pcQ9FuAjMQIvn+ZRs/a3wW/xrUPsa89lj0n/P8+RWMTmV6QiNFw5gPOqcYuNu6robmjh4zECBZNSyc28sySnR63ly0H6zhW2YrdYmTOpBTGZ0udXxEert2vh7x/LZ/6BdsrPBwqa8ZsMlA8MZnC3Pg+HRV99SfxHNuE5urCmJyHuWAhivXiqusIIcRIIB0VRS+tp5POJ/416LgSEYcxeQze0h1BH1Nf9Hl+ujb4hqeisYl89ZaB76joKdlKz5r/CzpuKljIt3aPC7lZ8H+X1OPdvzrouHXBXVgKl19UnEIMJed7vw15/9qv/hqm7KLwBSSEECOIdFQcxTRN40R1G22dbrJSIkmOtoHJCl79DXmKPbrfzUq26DigLuh4dIQFVdM4VtFKp9NDXlo0CTpLIupbnVTUdhAdYWFcZkyfmTI4s9nQ59MYnx2LpZ+4DPZooh0WnC79kmARNhNGRwyBK9XPUOyBnR31lJxqo6XdRUZSBGkJMoMnLh39bja0x9Dp9HCsshWzycCE7DjMJlkJKIQQF0uS6hGsvLaD3792kNpm/6ZABSgal8hn8+agHV+ve465YBHGlDF4Dq3VHTfEZ5IxoZCclO6gJeKykyN54HdbejsfKgrMnZTCvSsmYDYZ6XF7+fObh9l1tKF3ZXRKnJ0vXFdIXpo/Idiw9xTPfXCid220xWzg6uIslkcloXXobbRSMI9fxELVxYsfntSNa8GUNEzjknBtfwHUwNlsxRaFKadI99yPVTd28ftXD1LVcKYs4uT8+KDrxIUIN/P4RSHv35cO+Hhvxya8Pn8DmCiHmTuXFzBnUuA+DyGEEOdOpidGqE6nv6Pixwk1+Lf27T7eyFONkzEk5AScY8qdiXnSUoxJeViKbw4YV2xR2JZ+EYDPX+vfMHm2K4uzeP7Dkj6txDUNthys48l3/R0V//LWEXZ+IqEGqGtx8qtn99DR7eZgWTN/e/tIn82Gbo/Ka5vLOZRxEwSs7VSwzr8TQ2waVxZnMzkvPiCuMRnRXL8wD4MjFtvi+0Ax9n2AyYJt2RdDtmF2eXz88pndfRJqgAMnm3s7Kgox1ELdv9sTVvH21orehBqgo9vDH18/xImqtnCGKYQQI46sqR6h3tlWwbNrT+iOKcDP7p9FfPMBvFUHUAwmTPmzMGZO6btZqbEcz7GNaD2dGBNzMY/vu1mpu8fDpv21/o6Kpzcb7jxWzxub9RtIGA0K/373TH78xI6gDWA+ddkYDle0cOBks+54cqydn90zCc+xjajNlSj2GMzjF/cp9aVqGvtONLHrWAOapjF1bCIzztpsqLbV4Tm6vrdOtXn8YgwRoTcTrt97ir+9fSTo+A/uLe6z2VKIoXT2/WsYN59v/3lv0AYwsyYk8+UbJoc5SiGEGF5kTfUoFKprowZUNvSQMmE+5nHzgz7OmJiDMTFwRvtjDpuZK4qz+hx7eYP+0gsAn6qx/2Q/HRXrOkLGXt/qxGWMwF60MuhjDIpC0bhEisYFNqvpfUxMCtbZnwoeiI5QHRc/HpekWlwqzr5/Wzpc/XZUFEIIceEkqR4h1K4WfHUnUCx2jOkTidFZmvFJ0REWOp0ejla0YDIamJgT16ecH9C72bDD6SEnNYrk2MBuiA2tTspqO4i0mxmfHau7JOSTkuL66agYYSEmwkJ7l/6bv81ixGI2UNPURVVDFzFBNjm63D4OlTejaTAhOw6HTaejYn3JmY6K8VkB421dbo5XtmK1GJmYE3dO/6ZCXCrOvn9jHBZMRkOfpR+fJL+/QghxcSSpHuY0nxfXxifwHNsImv/NUnHEMnfqPbyDfsvslHgH+0828stn9+Dx+s+JsJm4ddlYFk1NB+BEdRt/fP0gDa2nNxsCM8cncd/KidgsJlweH3996zDbj9T3zjwnxti4anY2G9HvSpifHs3cSam8sbmcU41duo9ZOCWNpFg7T79/XHe8eEIyv31xP3tLznQtTI138MXrCntnidfsrOKl9SW9pfUsZgMr5+Wyan4uAGprLc41j6M2VfRew5g2Advl/4LBEYOqajy95jjrdlf31uKOdpj9a7IVRbejYlyUlcn5gWu5hRgKwe7fGQWJbDtcr3vOIun4KYQQF0U2Kg5zrq3P4Tm6vjehBtC6W4nb/ji3zk/mrOZ/RNrNFI1N4M0tFb0JNUBXj5e/vXWEw2XNtHW6eOy5vb1vyOBPznccbehdU/zE6qNsO1zfZylHY1sPL35YwhWzAmd9YyMtfG7lRAC+sGoSUY7ADYG3Lh1LdkoUy2ZkMF1n6UZuahTN7T19EmqA2uZufvnsHv5/e/cd3dZ95Qn8+1AJggUESYAgqcYidqpZEkVVyrIdO+6xo8SezGbjSN7EziTZlMkkOx57zh7vzPokcjITO8XJrp3Ys7Lc5aJiybKq1UWKFIsk9l7ATgBEefsHKFAg3gPtgSgS5Pdzjv/Qe3wPlxKfcfnD7947YnfifE0XXt1f49eretTpwduHa3GktBWi24mRD5/zS6gBwN1WBdu+3wAA3jtWhwNnm/2G2wyMOPHax5fx5TULAiYq6rRKPH5vnt+ebaLpEuz5dbo8SE0MbAFZlGfG2kIm1UREoeBKdRgTR21wVn0qfdLtwkbleeQ/9oBvouI8UzTWFiThn/+v9GAIEcDe001IT46BzSHdzflMVRdqVw7gVKV0n2r7qBs6rRL/9M2VOFbehmGbC2nJMSjOT4JO6/1xm2+OxrPbi3CsrA0NHUOIjlRjXYEFqSbvxn+lQoEnHyxARZ0VZ6q74PZ4UJAWj+SESDz1p9OSrztkc+JYeTvOVku12/Pae7oJRZH1EId6JM97Oq/C1lKDA2fbJc+7PSJ6Bux4dlsRjpS1oXfQjuQEPdYXJvOjc5oxDpe2yj6/pVd68Oz21bjaMoCKeis0KgVWZpuQs5CfshARhYpJdRjz9HfIDnEBAE9PE1ISo7B1c6bv2JDNiZ4Bu+w1TZ1D0AQZBOERRVTUWWVHlF+7x/3r04IW7ekj1Lh91XzZ84IgID8tHvlp8b5jcom873U7htDUKV9s1do9DFe39Eff1/S0NGLYLv/9N3YMwWyMxEOb0oPeh2i6NHXKF/p6RBHt1hGsyU/CmvykmxgVEdHsx6Q6jHknpwmQ3jntPS+KHrjbqiHaBqCIn4eI6CRoNUo4RqVHecfoNZOuupomKTaM1Wvg8YiobOzFsM2JhZYYySJHT1873D0NEHQxUFqyIAj+yaxj1I1L9d4EPntBHGIig8cVG6VBjF4Lm2NE8rw+QgWlPvhExaiYGCgVw7K/NMRGcUWaZrbJnt9YvRb9w6O+iYp5C+OgVimDXkNEN8bE9zUODZtdmFSHMUWUEcrUPLibpQePKJMyMbzzZxAHxldnlfOXYE32bThUJlOsVGhBenIsDp5rkTw/zxSF1blm7D3ViPp26VXhlMQo/PR3x2Ed8K6iCwBW5pjwrbtyoFErIY7aYP/kD3A1nPddI0QnQrf5cSjNGQCAQxdasOuTq76PsdUqBW5fmQqTQYfOvsAx5AK8ExN1WhXeOHRV5ntLhiojEY5TbwKewNRaiIhGbOYyLFtcjTNV8n8/RDPZ+sLkoM/viYp2HDzXDJfb+4ujPkKFR7Ys5so10RSTel+7Y9U8PLiBn3zOFqysCnMR678JITox4Lg6dzMc597zS6gBwN1Yii+Ln2ChxNaMW7ISsWlpChYkRUtub4jRa7DtnlwAwLfvzpVctf3Sqvl449BVX0INeNfRT1V24i/7qgEA9kMv+SXUACAOdmHko1/BYxtAeW0PXtlT7bcv1Ony4IMTjVi+OBH6Ce3xBACP3LYYScZI3L5yHgrT4zFRZmrs2ETFWERseixwoqI6AhG3fgeCUo1Ht2TCEh8ZcI8NSyxYlcNRzjSzBXt+F88zYN/pJl9CDXiLlF/64BJqmvpuYpREc4vc+9r7xxtw4GzzNEZGNxInKs4CossB55XP4G6vgaCJhDpjDdzt1XB8tlPmCgHah57FhQ4FymutUCkFrMg2IXdBnF+/5+auIRy72IahEW+f2+J8i1+/Z5vDhePl/hMVT1V24IMT8hMV//ffZkH13i8gt2VFs+oh/LY6FeV10hMVE2Ij8I//5RYcu9iO5q4hxOo1WFdogSV+vKOBKIq4WGsdn6iYnoBlmQlQKMa/N89A19hERevYRMX1UETG+s47XW6cquxEVWMvtGolVuWYsXieQebvk2jmmfj8rs4z4x9fOiXbA35FViKeeKDgJkdJNDf8aueFoO9r//rf1gTMW6CZiRMVZzlBpYUmeyOQvdF3bLTi4yBXiBD6mrEqZ2XQldfUCUWOE+m0Kty6ItXv2JufSm+9ALzdM1oamrFAJqEGvMWVjZ0G2fPd/XaolAp8aXXwIsfC9HjJFetrFDGJ0K78iux5tUqJtQUWrGXvXgpTE5/f3kGHbEINBC9wJKLQNAZ5vrr77WOds5iShTv+C85Sgi42+PlIAwaGR1E1NlExb6ERWs2EiYpjxYaDI6NYYI72Ww2+psM6grp270p17gLjpFMHDXExk8ZtCDJRUaf1TlRs6RpCU9cQYvVaZM03QDHhN3ybw4VL9VZ4RCBHphikpqkP1kE7LEY9x4vTrDPx+TVGR0CtUvj1p7/eZM8uEf3nfZ73NQp/TKpnKXXWejjL9kBqm4VgsODtSgH7zxzz7a3UaVX4akk6Ni5NAeBNOP+4uwI91+2NXpaZgG/fnQudVgXHqBsvvX/Ju8Vi7LwxRou7Vi/AEZmJiukpMUhdnI3h0lR4eqX2kAlQZ6/Heq2AV/fXSN5jZbYJv95V5vcxmsmgw+P35WGRxZuw7z/dhLeO1Po6nKhVCtxVtAD3rVsEwNta78V3ytFy3VTHjNRYfPf+fBiitJKvSxRO5J7f5YsTcfKSdGvKa9NUiejGW78kWfZ9rTjPwuFhswT/FWcpZVwytMWPAhNmKgoR0bhguh8fnWz0K1ayOVx4ZU81Kuqs6Bty4PldpX5vyABw/nI3/s/YRMWX91Th7HUJNQBYBxzYdegq7lgVOFExLlqLb3/ZW+QYcevjY+0A/SKDtuhrUBrnoWRZClZkBRZfpiXHoKvfHrAvrbPPhh2vl2LY7sTZ6i78x4HLfi0DnS4P3j1ah08vtMDp8uBXr1/wS6gB4EpzP/7tzbKA1yQKN8GeX4fTjXmmwL2AxflJWFvA7h9EUyXY+9qDG9OmISKaClypnsU0+VugSs2Ds+YoPCMDUCbMhzKjGO/++YLk14sA9p1uQnpKDOwyfazPVneitnUApyqlW845nG5oVEo8861VOFHejiGbE2nJMSjKMyNC4/1xUxrnQb/1X+CsOQ53dwMUumioFq+DMs67UqZQCHjigQJcqrfi7HUTFRPjIvH0n09Jvu6QzYljF9txtlp+uMu+003QqJR+nUmuV9c2iJqmPhYkUlg7XNoq+/yWXunG/9pWhPqOQVTUWaFRKbEyx8SfeaIpJve+tjQzgavUswiT6llOYbBAu+ph35+9ExXlpzA2dw1BG2RvlygCFXVWeII0jWnuGsIDG9Iwb3OG7NcImkho8rcEjT13oRG5141PnmyiYnPnEJq75ItB2npG0NAhP3ER8BZrMcGgcNYcpCBKFIH23hGsyjGzPSTRNJj4vkazC5PqOSZCoww6UTFWr0HsJPuKk4yTTFSM0kL0uOFuqYBoH4IyMQ0KQ+BHyy3dw2jsGERMpAY5C+L8Wt4B3i0p10ai5yyIm7SQKjZKg9ggExWjdGrERQf/3gycmEhhbrLnN1avRd+QA1WNvdColMhbZIRWzYmKRDeDOGqDq7kc8LihTMmFImArJIUzJtVzjEqpQHF+Ej6Rmbi2fkky0pNjZJvRLzBHY2WOGXtONaGubUDya4rNNgy/9iOII31jRwSoFq1AxKZtENRajNhd+MPuCpRd7fFdEx+jxfZ785CZagAAHDzXjDcOXfV9jK1SCthySyrMcTp09EpMVBSAdYXePtq7PpGbqGjBmrwkvPlpLVzuwA4IMXoNlmQkSF5LFC7WF1pkn9/55igcvdiGQ+db4B7r6x+pVeHrWzLZPpJoio1WHIDj1C7AafceUKigKbgd2tVfnd7A6IbhRp456KGN6UhLDvzteFWOCRuXJmO+ORpbN2dgYhv62CgNvu2bqJgjuer7lTVJMJ974bqEGgBEuOrOwH70ZQDAnz645JdQA0DPgLe4qn94FGVXe/DXfTV++0JdbhF7TjZhaUZCQHs8QQC+cXsWzHGRuO2WeVgqkRhnzTPg3nWLEKPX4Nt350Cl9P/uIjRKfOe+PKiUfCQovAV7fjNTYnHgbLMvoQaAEYcLf/6wEtWNvTc3UKI5xNVYBsexv4wn1ADgcWG09EOMXtw3fYHRDcWJinOUxyPi/OUulNdZoVIocEt2IrLmx/l9TVvPMI5dbMfgyCgWWmKw5rpiQwBwjLpx4lI76loHEBWpxtp8C+JrP8TohQ+kX1RQwnb3/8Q/vFwpO/7lwQ1pqG7sRUW99Bt8QmwE/um/rsTx8nY0dw4hNkqDdQUWmOLGx4qLoohLDb3edn8eEYUZCShMj/frZW0dsONoWRu6B+xIjtdjbUESoiO59YNmj4nP78rsRPyPP57EwIhT8uuXL07Ekw9yoiLRVBj54Dm4WyokzwnRCdB/7TlOVAwTnKhIARQKASuyTFiRZZL9Gku8Hg9tSpc9r9UosWlpCjaN9bYGgJHTTfIvKrphbaoPMk/RW+TY1DUse7673w6FIOC2WwLb9l0jCALyFhqRF6QYxBgTgXvH+lYTzUYTn9/eQYdsQg0gaJEvEYXGY5V/bxQHu70r2Jrg9Uo08zGpDlP2URfKa61wuj3Inh8nuRXjSnM/OnpHYIrT+fYqX69/yIHKhrGJiouMN2REqkJvgHQJpFdkXAIA+f+5GKK0MEQFmzylumlFVT39dtQ090GjUqIgzQgNi7kojEVGqIJOVDREaSGKItxtVRCHrFAYLFCa2D+X6EYQIuMg2qTrkKDRASoOHpsNmFSHocOlrdh58DJsDm/6qlQIKFmWgq9tyYRCENDVZ8Nv376Ixo7xlafUxCg88WA+zHGREEUROw9e8dtbqdUo8dVN6ShZnhpSbOrsjXBWHZY8pzRnYn5mJlITeyVXxQR4i6xMcTr8dZ/05Km1+UkBXUJuNLfHg7/srcHRsjZf60B9hAqP3rYYRXkckEHhSatWYnWOGUcvSk88XZsegZFdP4enb/y8wpQO3W1PQqGPk7yGiD4fdfYG755qqXOL10Fgr+pZgf+KYaay3oqXP6ryJdQA4PaI+PhsMz76rAEeUcTzu0r9EmrA+9HujtdL4fZ4sPdUE/adbvIrVnKMuvGXfTUor/UvIPyilKZ0aFY+FHBciIpHRMk2AMDj9+YidkLrOgHA17dkIiUxCpuWpmBVTuC2lIyU2JsyeeqdI3U4XNrq14t72O7CS+9XorZVZqWBKAxsvTUDC5KiA46vyzdj6ZU/+iXUAODpvArb3udvUnREs5c6pwSqtFUBxxXmDGhXfmUaIqKpwELFMPObN8pw4Uq35LlYvQbf+FIW/v3Ni7LXP/FAPl77+DJ6B6UHwOSnGfHfv7o05DjdvS1wXT4O0T4EhSkN6owiCNd9vGUfdeFERQca2gcRo/cWOZqNkX73qG7sxdnqLrg8IgrSjFiSnjDlq9ROlxs//LdjGHG4JM8X5Zmx/Z68KY2BaCq5PR6cr+nGpXor1GMTFRcMl8N+6I+y1+ju+QeoLFk3MUqi2cnVVg1X3RnA7YJqXiGU85dwlTrMsFBxFglWTNQ/PIq61v6g19e2Dcgm1ADQEqRI8ItQxqVAed0kx4kiNCqULEuRPQ8AWfPjAjqSTDXroEM2oQaCT6sjCgdKhQK3ZJtwS/b4p0H2z4IUGGOsyIpJNVHIVJYs/oI6izGpDjOGaC26++2S57RqJRJig1cPJ8bqEKFR+vWA9rt/lAaiKKKmqQ9dfXaYjdJFjtYBu28iW36a0a/VHgC43B6U11kxbHNikSUGyQn6gHs0dw6hoWMQ0ZEa5C2Kg3LCb+sjdhfK63rgdovIXRgXMCluKuI0RGmgVAh+W2P8/n4mmchINN0+z3PhGbLC3VoJqDRQzSuYdM+0EMk91UREk2FSHWY2FCbjSrP0anRRnhlFuUnY9clVydVWnVaJ1blmtHQN48A56YlrhekJeOpPp9DSPb5iPd8chSceKECiQQePR8Sr+2vw6YXxPcc6rRJbN2diw5JkAEB5bQ9e+qDSr4PHsswEbLsnFxEaFUbsTrz4bgUq6qy+83HRWmy/J9e3Mr3/TBPe+rQWDud4MeatK1K9Qy0EAW09w3jh7fIpiXNZZgLOVHfJ/v0TzVSTPReixwPH8b/CWXkIEMe6gKh10Ky4H1CqAXdgyz1BFwvVgiU35xsgIgpj3MgTZtYWJGGdxDjhRZYYPLwpA1qNEt+5Pz+g7ZxGrcDj9+ZDp1XhwY1pyEiJDbjH6lwzjpa1+r0hA0BjxxB+/UYZPKKI3cfr8cn5Fr8iPpvDjZc/qkJVQy+6+mz497cuBrTEO3+5G6/srQYAvPR+pV9CDXh76P76jTL0DzlQeqUb//HxZV9CDXiLMfedbsL+001wuT3Y8XrplMUpwtstZaJbl6f6fWRONJN8nudi9Px7cF46OJ5QA4DThtHP/h80S78MKCess2h0iLjtCQgKrr8QEU2G/6cMM4Ig4FtfzsGGpck4U9UJp8uDvEVGLM0YL+LLW2TEv35nDY5fbPf2qTbosLbAghi9t+OGTqvCz/5mOUqvdPtNVOwddODkpQ7J123tHkbZ1W4clFnhFuFdXU4yRmJUpg/u6cpOlCxLQalMoaV91I3DZW2oapAfl7z/TBMMUfJbYLxx9oQU54XL3fiXx4tQ2zaIyoZeaNUKrM41Y2FS4Gh3opniXE1X0Oei/GoX0ioOyFwtwtPTCP3XnoOz5ijEwR4o4ixQZ66FECFdkENERP6YVIepjJRYydXma2IiNfjS6vmy5xWCgGWZiViWmeg79vbh2qCvebVlAINBJrK1dA/L7kUGvKvNVQ29QScqtnQNBay0Xa9nwIGGjsFJ4uwPOc7OPjtWZpuwkivTFCYmKzJubrVikV3+2XFbW6DQx0G77J4bHRoR0ZzApHoO6x104FK9FSqlAgVp8ZJTGa9nitNBo1LIrvDGRWlhiAp+j6T4wILF6xmitIiL0gadqJgQGxH0HmZD6HEaJvTRJprpJnt+4+KiAKUGcEs/Wwq9YQqiIiKaO7ineg7yiN4ivp++eBx/+qASv3+vAj/67TGMOJzQaqRHcUfp1FidY8bqXLPsfTcsSfYVAUpZnBqLldkmzDdJf5wsCMD6JcnYsFT+HusKLFidmxQ0zlW5ocdpmST5J5ppVuWYgz4XK7ItUGcUyV6vzt44VaEREc0JTKrnoL0nG/1GlAOAw+nGG4dq8aVV86FR+f9YRIwVP2rUSmzdnIFFlsCJbBuXJqMoz4y05Bg8XJKOiSNaEmIj8NjduQCA7ffmBayqCQLwN7ctRkqC3neviRbPM+DBDWmIjFDhO/flTXmcROHk8zwX2qKtUCQuCrhWnb0Jqow1NytUIqJZiRMV5xiPKOInLxyXHQCTt8iIbXfn4lh5G7r67EiK06G4wIIonXr8Hh4RpVe6UVFvhWZsItsii38RX7t1BMfL2zFscyItOQarckxQq8ZX0RxON05e8k5UjI5Uo7jAApPBv8f2leZ+nKnuhNstoiDdiPy0eCiE8TR4YHh0yuMkCjeTPReixwNX4wW4mysAlRrqtFVQmtKmMWIiovARbKIik+o5ZsjmxN/9+ojs+bhoLX75xNqbGBERERFReOCYcvKJ0Cih0yphc8hNVNRCFEVUNvSiu98Oc5xOclR4d78NlQ29UKsUWJKeAJ3W/0fJ6fKg7GoPhu1OpFlikCqxj7qhfXBsoqIaBWnxUCn9P7YetjtRdtU7UTFvkTFgy4goinC3VsIz2AVFjBmq5OwvHCcRERHRjTCjMozdu3fjxRdfhNPpxDe/+U08+uij0x3SrKNSKrA234KPz0r3cV6SHo9f/PEk2q0jvmOpiXo8+WABTHGR8HhEvLK3GkfKWnHtMw6tRomtJRnYtCwFAFB2tRsvvV+JIdt4W7vC9Hg8fm8edFoVhmxOvPhOOSqv60cdq9dg+715yFngTeD3nmrE24drfR08lAoBJctS8PUtmRAEAe7eVtj3/Qae/nbfPRTGVOhu/zsoYkyfK04iIiKiG2XGFCp2dHRgx44deO211/Duu+9i586duHLlynSHNSs9uDENi1MDe1wX5yfhcGmLX0INAM1dw76JbO8dq8Ph0vFEFQAco278ZW81Kuut6Oyz4bdvl/sl1ABQdrUHL++pAgC89P4lv4QaAPqHR/GbN8vQN+TAhcvd2Hnwil9LPLdHxMdnm7H3VBNEtwu2j37pl1ADgMfaDNueHRBFz6RxEhEREd1IM2al+vjx4ygqKoLBYAAA3HHHHdizZw+efPLJ6Q1sForQqPDTR5ejvLYH5bXePtUrshPR02/H8fJ2yWvaekZQeqUbB8+1SJ73TipsRlJ8JJwy/aHPVHWhZnkfLl7tkTzvGHXjcGkrqhv7ZGP/+GwTNse3QhySvoenrw2OhjIcPCc95OJanDkLjbKvQURERPRFzZikurOzE4mJ49P9TCYTysrKpjGi2U0hCChMT0BheoLvWNkV6UT1mtrWgYAV6Ou19gzDE6Tu1SOKqG4MPlGxtXs46ERF64ADo93BV5r721sxZJMfENPaE3zyHBEREdEXNWOSaqkmJIIwsYuwPLlKTPr85ifLjz0HgMwFRmjPNsMxKl3kaDbqYUnUo0xmJRoAstMSgCN1sudTzDGwDjpkJyrqdWrEJafAGuT3Lcv8ZGg1/UHjTEwM7GFNRERE9J81Y5Jqs9mMM2fO+P7c2dkJk8n0ua9nS73Q5c6LRYRGCbtEMhodqUZOagxW55hxuLRV8vo1uSaYjZHY+1mD5PmseQZkWqKxwByNho7A7RmCANySGY/oCCWuNPdL3mNtfhJGk1IBdQTgtAfeIyIablMuVufUBo2zq0t6ewgRERGRnGAt9WZMoWJxcTFOnDgBq9UKm82Gffv2YcOGDdMd1pyi06rw3fvzoVUrJY+rVUp8tSQD6SkxAdduXp6CorwkLLLE4GubMwImFZridHjs7hwAwOP35cEY498eTyEI+Ns7smCJ12PDkmSszU8KeI3s+QY8sCENgkYH3ZYnAJX/PaDRIeK2JyGoNJPGSURERHQjzajhL7t378bvf/97OJ1OPPTQQ9i2bdvnvpYr1TfO4MgoTpS3o2usT3VxfhIiI66bVCiKuHi1Z3xSYbYJC5L8t1N09o7gREUHhm1OLEqOwcpsk18f6lGnG6cqO9E41qe6ON+C+Fj/fdC1rQM4W90Jt0dEfpoReQuNfluCPPZBuGqOe/tUx5qhziyGoNV/oTiJiIiIPi9OVCQiIiIiClFYbP8gIiIiIgpXTKqJiIiIiELEpJqIiIiIKERMqomIiIiIQsSkmoiIiIgoREyqiYiIiIhCxKSaiIiIiChETKqJiIiIiELEpJqIiIiIKERMqomIiIiIQsSkmoiIiIgoREyqiYiIiIhCxKSaiIiIiChETKqJiIiIiELEpJqIiIiIKERMqomIiIiIQsSkmoiIiIgoREyqiYiIiIhCpJruAG4UhUKY7hCIiIiIaBYLlm8KoiiKNzEWIiIiIqJZh9s/iIiIiIhCxKSaiIiIiChETKqJiIiIiELEpJqIiIiIKERMqomIiIiIQsSkmoiIiIgoREyqiYiIiIhCxKSaiIiIiChETKqJiIiIiELEpJqmxcmTJ/GNb3xjusMgmlOam5uRlZWFp556yu94ZWUlsrKy8NZbb01TZESzX3NzMzZv3hxwPCsraxqioanApJqIaA4xGAw4cuQI3G6379iHH34Io9E4jVEREYU/JtVERHOIXq9HTk4OTp8+7Tt27NgxFBcXT2NUREThTzXdARAR0c115513Yu/evSgqKkJZWRmysrIgiuJ0h0U063V2duK+++6b7jBoijCpJiKaY0pKSvD888/D4/Hgo48+wp133okPP/xwusMimvVMJhPeffddv2PcUz17cPsHEdEcExUVhezsbJw9exafffYZt34QEd0ATKqJiOagO++8E7/85S+Rn58PlYofWhIRhYpJNU2bM2fOYNmyZb7/Jrb5IqKpU1JSgsrKStx1113THQoR0awgiKxOISIiIiIKCVeqiYiIiIhCxKSaiIiIiChETKqJiIiIiELEpJqIiIiIKERMqomIiIiIQsTmpEREs4Db7cYrr7yC3bt3w+12w+l0oqSkBN///vfx1FNPITMzE4899th0h0lENGsxqSYimgWefvpp9Pf34+WXX0Z0dDRGRkbw4x//GL/4xS+gVCqnOzwiolmPSTURUZhramrC7t27cfToUURFRQEAIiMj8cwzz+D8+fM4ePCg72vfeOMN7Ny5E06nE/39/di2bRseeeQRdHV14e///u/R29sLANi4cSN+8IMfyB4nIiJ/3FNNRBTmLl26hIyMDF9CfU1iYiJuv/1235+Hh4exa9cu/OEPf8A777yDHTt24LnnngMAvP7660hNTcXbb7+NV199FQ0NDRgcHJQ9TkRE/rhSTUQU5hQKBTwez6Rfp9fr8bvf/Q6ffvop6uvrUVVVhZGREQDA+vXrsX37drS1taG4uBg/+tGPEB0dLXuciIj8caWaiCjMFRYWora2FkNDQ37HOzo6sH37dtjtdgBAe3s77r//frS0tGDFihV+2zgKCwtx4MABbN26FS0tLXj44Ydx7tw52eNEROSPK9VERGHObDbjnnvuwc9//nM8++yziIqKwtDQEJ5++mkYDAYoFN71k/LychiNRnz3u9+FIAh48cUXAXg7h+zYsQOiKOInP/kJbr31VlRXV6O+vh4HDx6UPL58+fLp/JaJiGYcQRRFcbqDICKi0LhcLrzwwgvYt28flEolRkdHsWXLFnzve9/ztdR75JFH8MMf/hB1dXXQ6XQoLCzE/v378eqrryI6Oho/+9nP0NHRAY1Gg6ysLDzzzDPo7++XPK7RaKb7WyYimlGYVBMRERERhYh7qomIiIiIQsSkmoiIiIgoREyqiYiIiIhCxKSaiIiIiChETKqJiIiIiELEpJqIiIiIKERMqomIiIiIQsSkmoiIiIgoRP8fdzzOs8ETqYoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(12,10)})  # 不同性别下，访问在线资源和成绩的相关性\n",
    "sns.swarmplot(x='Class',y='VisitedResources',hue='Gender',data=df,order=['L','M','H'],size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>VisitedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Raisedhands</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.691572</td>\n",
       "      <td>0.643918</td>\n",
       "      <td>0.339386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VisitedResources</th>\n",
       "      <td>0.691572</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.594500</td>\n",
       "      <td>0.243292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <td>0.643918</td>\n",
       "      <td>0.594500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.417290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discussion</th>\n",
       "      <td>0.339386</td>\n",
       "      <td>0.243292</td>\n",
       "      <td>0.417290</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Raisedhands  VisitedResources  AnnouncementsView  \\\n",
       "Raisedhands           1.000000          0.691572           0.643918   \n",
       "VisitedResources      0.691572          1.000000           0.594500   \n",
       "AnnouncementsView     0.643918          0.594500           1.000000   \n",
       "Discussion            0.339386          0.243292           0.417290   \n",
       "\n",
       "                   Discussion  \n",
       "Raisedhands          0.339386  \n",
       "VisitedResources     0.243292  \n",
       "AnnouncementsView    0.417290  \n",
       "Discussion           1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 相关矩阵\n",
    "corrDF=df[['Raisedhands','VisitedResources','AnnouncementsView','Discussion']].corr()\n",
    "corrDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制热力图\n",
    "sns.heatmap(corrDF,annot=True,cmap='YlGnBu')#annot=True参数就是显示数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  # 训练、测试\n",
    "from sklearn.linear_model import LogisticRegression   # 逻辑回归模型\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score            # 准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('../csv/StudentPerformance.csv')\n",
    "\n",
    "#修改列名\n",
    "df.rename(index=str,columns={\n",
    "    'gender':'Gender',\n",
    "    'NationalITy':'Nationality',\n",
    "    'raisedhands':'Raisedhands',\n",
    "    'VisITedResources':'VisitedResources'\n",
    "},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 17)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KW             179\n",
       "Jordan         172\n",
       "Palestine       28\n",
       "Iraq            22\n",
       "lebanon         17\n",
       "Tunis           12\n",
       "SaudiArabia     11\n",
       "Egypt            9\n",
       "Syria            7\n",
       "Lybia            6\n",
       "Iran             6\n",
       "USA              6\n",
       "Morocco          4\n",
       "venzuela         1\n",
       "Name: Nationality, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Nationality'].value_counts()  # 来自不同国家数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      M\n",
       "1      M\n",
       "2      L\n",
       "3      L\n",
       "4      M\n",
       "      ..\n",
       "475    L\n",
       "476    M\n",
       "477    M\n",
       "478    L\n",
       "479    L\n",
       "Name: Class, Length: 480, dtype: object"
      ]
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     "execution_count": 35,
     "metadata": {},
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   "source": [
    "df['Class']\n",
    "#df['Gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "     F  M\n",
       "0    0  1\n",
       "1    0  1\n",
       "2    0  1\n",
       "3    0  1\n",
       "4    0  1\n",
       "..  .. ..\n",
       "475  1  0\n",
       "476  1  0\n",
       "477  1  0\n",
       "478  1  0\n",
       "479  1  0\n",
       "\n",
       "[480 rows x 2 columns]"
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     "execution_count": 36,
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    "pd.get_dummies(df['Gender'])  # 把性别男女的F,M定义为0-1"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('Class',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "   Raisedhands  VisitedResources  AnnouncementsView  Discussion  Gender_F  \\\n",
       "0           15                16                  2          20         0   \n",
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       "3           30                25                  5          35         0   \n",
       "4           40                50                 12          50         0   \n",
       "\n",
       "   Gender_M  Nationality_Egypt  Nationality_Iran  Nationality_Iraq  \\\n",
       "0         1                  0                 0                 0   \n",
       "1         1                  0                 0                 0   \n",
       "2         1                  0                 0                 0   \n",
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       "2             0                         1                          0   \n",
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       "4             0                         1                          0   \n",
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       "[5 rows x 72 columns]"
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    "X=df.drop('Class',axis=1)\n",
    "Y=df['Class']\n",
    "X=pd.get_dummies(X)#将所有的分类型特征转换为数字，虚拟变量：dummy variables\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     Egypt  Iran  Iraq  Jordan  KW  Lybia  Morocco  Palestine  SaudiArabia  \\\n",
       "0        0     0     0       0   1      0        0          0            0   \n",
       "1        0     0     0       0   1      0        0          0            0   \n",
       "2        0     0     0       0   1      0        0          0            0   \n",
       "3        0     0     0       0   1      0        0          0            0   \n",
       "4        0     0     0       0   1      0        0          0            0   \n",
       "..     ...   ...   ...     ...  ..    ...      ...        ...          ...   \n",
       "475      0     0     0       1   0      0        0          0            0   \n",
       "476      0     0     0       1   0      0        0          0            0   \n",
       "477      0     0     0       1   0      0        0          0            0   \n",
       "478      0     0     0       1   0      0        0          0            0   \n",
       "479      0     0     0       1   0      0        0          0            0   \n",
       "\n",
       "     Syria  Tunis  USA  lebanon  venzuela  \n",
       "0        0      0    0        0         0  \n",
       "1        0      0    0        0         0  \n",
       "2        0      0    0        0         0  \n",
       "3        0      0    0        0         0  \n",
       "4        0      0    0        0         0  \n",
       "..     ...    ...  ...      ...       ...  \n",
       "475      0      0    0        0         0  \n",
       "476      0      0    0        0         0  \n",
       "477      0      0    0        0         0  \n",
       "478      0      0    0        0         0  \n",
       "479      0      0    0        0         0  \n",
       "\n",
       "[480 rows x 14 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FEATURE1=pd.get_dummies(df['Nationality']) # 重新定义国家的表示 \n",
    "FEATURE2=pd.get_dummies(df['Gender'])      # 重新定义性别的表示 \n",
    "FEATURE1                                   # 显示定义后的国家内容，1即表示属于该国家"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Egypt</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Egypt  Iran  Iraq  Jordan  KW  Lybia  Morocco  Palestine  SaudiArabia  \\\n",
       "0        0     0     0       0   1      0        0          0            0   \n",
       "1        0     0     0       0   1      0        0          0            0   \n",
       "2        0     0     0       0   1      0        0          0            0   \n",
       "3        0     0     0       0   1      0        0          0            0   \n",
       "4        0     0     0       0   1      0        0          0            0   \n",
       "..     ...   ...   ...     ...  ..    ...      ...        ...          ...   \n",
       "475      0     0     0       1   0      0        0          0            0   \n",
       "476      0     0     0       1   0      0        0          0            0   \n",
       "477      0     0     0       1   0      0        0          0            0   \n",
       "478      0     0     0       1   0      0        0          0            0   \n",
       "479      0     0     0       1   0      0        0          0            0   \n",
       "\n",
       "     Syria  Tunis  USA  lebanon  venzuela  F  M  \n",
       "0        0      0    0        0         0  0  1  \n",
       "1        0      0    0        0         0  0  1  \n",
       "2        0      0    0        0         0  0  1  \n",
       "3        0      0    0        0         0  0  1  \n",
       "4        0      0    0        0         0  0  1  \n",
       "..     ...    ...  ...      ...       ... .. ..  \n",
       "475      0      0    0        0         0  1  0  \n",
       "476      0      0    0        0         0  1  0  \n",
       "477      0      0    0        0         0  1  0  \n",
       "478      0      0    0        0         0  1  0  \n",
       "479      0      0    0        0         0  1  0  \n",
       "\n",
       "[480 rows x 16 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature=pd.concat([FEATURE1,FEATURE2],axis=1)  # 合并国家与性别，并输出显示\n",
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219    M\n",
       "183    M\n",
       "453    M\n",
       "380    L\n",
       "308    M\n",
       "Name: Class, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据拆分为训练集与测试集，其中测试集用于评测模型的优劣,其中选取数据集中20%用于测试\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "y_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((384, 72), (96, 72), (384,), (96,))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape # 显示训练与测试信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练并测试模型\n",
    "# 逻辑回归模型\n",
    "Logit=LogisticRegression(random_state=0)\n",
    "Logit.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict ['H' 'M' 'M' 'L' 'H' 'H' 'H' 'H' 'M' 'L' 'M' 'L' 'H' 'M' 'M' 'M' 'L' 'H'\n",
      " 'M' 'H' 'L' 'M' 'M' 'H' 'M' 'L' 'H' 'H' 'M' 'H' 'H' 'H' 'L' 'M' 'H' 'L'\n",
      " 'M' 'M' 'L' 'H' 'M' 'L' 'H' 'L' 'M' 'M' 'H' 'M' 'M' 'H' 'M' 'L' 'M' 'M'\n",
      " 'M' 'H' 'H' 'L' 'M' 'H' 'H' 'L' 'H' 'M' 'M' 'M' 'M' 'H' 'L' 'H' 'M' 'L'\n",
      " 'M' 'M' 'M' 'M' 'M' 'L' 'H' 'H' 'L' 'H' 'L' 'L' 'M' 'H' 'L' 'H' 'H' 'M'\n",
      " 'M' 'L' 'M' 'M' 'H' 'M']\n"
     ]
    }
   ],
   "source": [
    "# 模型预测\n",
    "\n",
    "Predict=Logit.predict(X_test) # 预测\n",
    "print('Predict',Predict)      # 输出预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.7604166666666666\n"
     ]
    }
   ],
   "source": [
    "# 模型测试\n",
    "Score=accuracy_score(y_test,Predict)  # 测试与预测的相似度\n",
    "print('accuracy',Score)               # 输出准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个包含不同c取值的列表[正则化系数入的倒数，必须是正浮点数]\n",
    "from inspect import Parameter\n",
    "\n",
    "\n",
    "C_grid=[0.2,0.4,0.6,0.8,1,1.2,1.4,1.6]\n",
    "\n",
    "# 创建一个包含不同penalty取值列表[惩罚项，str类型，可选参数为l1,l2，默认为l2]\n",
    "penalty_grid=[\"l2\",\"none\"]\n",
    "\n",
    "# 创建一个包含不同class_weight取值的列表\n",
    "class_weight_grid=['balanced',None]# balanced:adjust weights\n",
    "\n",
    "# 组合元组列表\n",
    "parameters=[(C_,penalty_,class_weight_) for C_ in C_grid for penalty_ in penalty_grid for class_weight_ in class_weight_grid]\n",
    "#parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32,\n",
       " [(0.2, 'l2', 'balanced'),\n",
       "  (0.2, 'l2', None),\n",
       "  (0.2, 'none', 'balanced'),\n",
       "  (0.2, 'none', None),\n",
       "  (0.4, 'l2', 'balanced'),\n",
       "  (0.4, 'l2', None),\n",
       "  (0.4, 'none', 'balanced'),\n",
       "  (0.4, 'none', None),\n",
       "  (0.6, 'l2', 'balanced'),\n",
       "  (0.6, 'l2', None),\n",
       "  (0.6, 'none', 'balanced'),\n",
       "  (0.6, 'none', None),\n",
       "  (0.8, 'l2', 'balanced'),\n",
       "  (0.8, 'l2', None),\n",
       "  (0.8, 'none', 'balanced'),\n",
       "  (0.8, 'none', None),\n",
       "  (1, 'l2', 'balanced'),\n",
       "  (1, 'l2', None),\n",
       "  (1, 'none', 'balanced'),\n",
       "  (1, 'none', None),\n",
       "  (1.2, 'l2', 'balanced'),\n",
       "  (1.2, 'l2', None),\n",
       "  (1.2, 'none', 'balanced'),\n",
       "  (1.2, 'none', None),\n",
       "  (1.4, 'l2', 'balanced'),\n",
       "  (1.4, 'l2', None),\n",
       "  (1.4, 'none', 'balanced'),\n",
       "  (1.4, 'none', None),\n",
       "  (1.6, 'l2', 'balanced'),\n",
       "  (1.6, 'l2', None),\n",
       "  (1.6, 'none', 'balanced'),\n",
       "  (1.6, 'none', None)])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(parameters),parameters # 输出长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看top5最有参数结果\n",
    "result_accuracy={}\n",
    "for parameter in parameters:\n",
    "    result_accuracy[parameter]=LogisticRegression(penalty=parameter[1],\n",
    "    C=parameter[0],\n",
    "    solver='lbfgs',\n",
    "    random_state=0,\n",
    "    class_weight=parameter[2]\n",
    "    ).fit(X_train,y_train).score(X_test,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0.2, 'l2', 'balanced'): 0.7395833333333334,\n",
       " (0.2, 'l2', None): 0.75,\n",
       " (0.2, 'none', 'balanced'): 0.7708333333333334,\n",
       " (0.2, 'none', None): 0.78125,\n",
       " (0.4, 'l2', 'balanced'): 0.75,\n",
       " (0.4, 'l2', None): 0.75,\n",
       " (0.4, 'none', 'balanced'): 0.7708333333333334,\n",
       " (0.4, 'none', None): 0.78125,\n",
       " (0.6, 'l2', 'balanced'): 0.7395833333333334,\n",
       " (0.6, 'l2', None): 0.7604166666666666,\n",
       " (0.6, 'none', 'balanced'): 0.7708333333333334,\n",
       " (0.6, 'none', None): 0.78125,\n",
       " (0.8, 'l2', 'balanced'): 0.7708333333333334,\n",
       " (0.8, 'l2', None): 0.7708333333333334,\n",
       " (0.8, 'none', 'balanced'): 0.7708333333333334,\n",
       " (0.8, 'none', None): 0.78125,\n",
       " (1, 'l2', 'balanced'): 0.7708333333333334,\n",
       " (1, 'l2', None): 0.7604166666666666,\n",
       " (1, 'none', 'balanced'): 0.7708333333333334,\n",
       " (1, 'none', None): 0.78125,\n",
       " (1.2, 'l2', 'balanced'): 0.75,\n",
       " (1.2, 'l2', None): 0.78125,\n",
       " (1.2, 'none', 'balanced'): 0.7708333333333334,\n",
       " (1.2, 'none', None): 0.78125,\n",
       " (1.4, 'l2', 'balanced'): 0.7604166666666666,\n",
       " (1.4, 'l2', None): 0.7708333333333334,\n",
       " (1.4, 'none', 'balanced'): 0.7708333333333334,\n",
       " (1.4, 'none', None): 0.78125,\n",
       " (1.6, 'l2', 'balanced'): 0.7708333333333334,\n",
       " (1.6, 'l2', None): 0.7708333333333334,\n",
       " (1.6, 'none', 'balanced'): 0.7708333333333334,\n",
       " (1.6, 'none', None): 0.78125}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_accuracy # 预测结果\n",
    "#此时可以看到不同参数下，特征筛选后的数据放入逻辑回归的模型中精确度的不同，于是我们通过accuracy排序，找到前五最优参数并展示出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter_list</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(1.6, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0.2, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(1.4, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0.4, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(1.2, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       parameter_list  accuracy\n",
       "31  (1.6, none, None)   0.78125\n",
       "3   (0.2, none, None)   0.78125\n",
       "27  (1.4, none, None)   0.78125\n",
       "7   (0.4, none, None)   0.78125\n",
       "23  (1.2, none, None)   0.78125"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "racc=pd.DataFrame(list(result_accuracy.items()),\n",
    "                    columns=['parameter_list','accuracy']).sort_values(by='accuracy',ascending=False)[:5]  # 显示前5个\n",
    "racc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter_list</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(1.6, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0.2, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(1.4, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0.4, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(1.2, none, None)</td>\n",
       "      <td>0.78125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      parameter_list  accuracy\n",
       "0  (1.6, none, None)   0.78125\n",
       "1  (0.2, none, None)   0.78125\n",
       "2  (1.4, none, None)   0.78125\n",
       "3  (0.4, none, None)   0.78125\n",
       "4  (1.2, none, None)   0.78125"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "racc.reset_index(drop=True)# 将前面的序列变成0，1，2，3，4，5 方便查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "决策树建模效果：\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           H       0.52      0.74      0.61        23\n",
      "           L       0.73      0.70      0.71        23\n",
      "           M       0.68      0.56      0.62        50\n",
      "\n",
      "    accuracy                           0.64        96\n",
      "   macro avg       0.64      0.66      0.64        96\n",
      "weighted avg       0.65      0.64      0.64        96\n",
      "\n",
      "分类正确率：0.6354166666666666\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>决策树</td>\n",
       "      <td>0.635417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model  accuracy score\n",
       "0   决策树        0.635417"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建模并评估\n",
    "keys=[]\n",
    "scores=[]\n",
    "models={'决策树':DecisionTreeClassifier(random_state=2021)}# random_state=2021就是种子，种子可以不给，不给的话分类正确率会发生变化\n",
    "# 种子决定了决策树的过程中它去做样本子空间划分的特征拆解的时候，它是有一定的随机性，比如说选什么样的特征具有一定随机性，所以固定的时候，就会按照特定的方式来进行特征拆解\n",
    "# 此时这个模型只用了决策树模型，还可以加其他的模型，就在models里面加就好了\n",
    "for k,v in models.items():\n",
    "    mod=v\n",
    "    mod.fit(X_train,y_train)#模型训练\n",
    "    pred=mod.predict(X_test)#模型预测\n",
    "    print(str(k)+'建模效果：'+'\\n')\n",
    "    print(classification_report(y_test,pred,target_names=['H','L','M']))\n",
    "    acc=accuracy_score(y_test,pred)\n",
    "    print('分类正确率：'+str(acc))\n",
    "    print('\\n'+'\\n')\n",
    "    keys.append(k)\n",
    "    scores.append(acc)\n",
    "    table=pd.DataFrame({'model':keys,'accuracy score':scores})\n",
    "    \n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个包含不同criterion取值的列表\n",
    "criterion_grid=['gini','entropy']\n",
    "\n",
    "# 创建一个包含不同max_depth取值的列表\n",
    "depth_grid=[1,2,3,4,5,6,7,8]\n",
    "\n",
    "# 创建一个包含不同class_weight取值的列表\n",
    "class_weight_grid=['balanced',None]\n",
    "\n",
    "#组合元组列表\n",
    "parameters=[(criterion_,depth_,weight_) for criterion_ in criterion_grid for depth_ in depth_grid for weight_ in class_weight_grid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('gini', 1, 'balanced'),\n",
       " ('gini', 1, None),\n",
       " ('gini', 2, 'balanced'),\n",
       " ('gini', 2, None),\n",
       " ('gini', 3, 'balanced'),\n",
       " ('gini', 3, None),\n",
       " ('gini', 4, 'balanced'),\n",
       " ('gini', 4, None),\n",
       " ('gini', 5, 'balanced'),\n",
       " ('gini', 5, None),\n",
       " ('gini', 6, 'balanced'),\n",
       " ('gini', 6, None),\n",
       " ('gini', 7, 'balanced'),\n",
       " ('gini', 7, None),\n",
       " ('gini', 8, 'balanced'),\n",
       " ('gini', 8, None),\n",
       " ('entropy', 1, 'balanced'),\n",
       " ('entropy', 1, None),\n",
       " ('entropy', 2, 'balanced'),\n",
       " ('entropy', 2, None),\n",
       " ('entropy', 3, 'balanced'),\n",
       " ('entropy', 3, None),\n",
       " ('entropy', 4, 'balanced'),\n",
       " ('entropy', 4, None),\n",
       " ('entropy', 5, 'balanced'),\n",
       " ('entropy', 5, None),\n",
       " ('entropy', 6, 'balanced'),\n",
       " ('entropy', 6, None),\n",
       " ('entropy', 7, 'balanced'),\n",
       " ('entropy', 7, None),\n",
       " ('entropy', 8, 'balanced'),\n",
       " ('entropy', 8, None)]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#参数调优\n",
    "result_accuracy={}\n",
    "for parameter in parameters:\n",
    "    result_accuracy[parameter]=DecisionTreeClassifier(\n",
    "    criterion=parameter[0],\n",
    "    max_depth=parameter[1],\n",
    "    class_weight=parameter[2],\n",
    "    random_state=2021\n",
    "    ).fit(X_train,y_train).score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter_list</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(entropy, 4, None)</td>\n",
       "      <td>0.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(gini, 4, None)</td>\n",
       "      <td>0.760417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(entropy, 6, None)</td>\n",
       "      <td>0.729167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(entropy, 4, balanced)</td>\n",
       "      <td>0.729167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(gini, 6, balanced)</td>\n",
       "      <td>0.729167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            parameter_list  accuracy\n",
       "23      (entropy, 4, None)  0.781250\n",
       "7          (gini, 4, None)  0.760417\n",
       "27      (entropy, 6, None)  0.729167\n",
       "22  (entropy, 4, balanced)  0.729167\n",
       "10     (gini, 6, balanced)  0.729167"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "racc=pd.DataFrame(list(result_accuracy.items()),\n",
    "                    columns=['parameter_list','accuracy']).sort_values(by='accuracy',ascending=False)[:5]  # 输出前5列\n",
    "racc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter_list</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(entropy, 4, None)</td>\n",
       "      <td>0.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(gini, 4, None)</td>\n",
       "      <td>0.760417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(entropy, 6, None)</td>\n",
       "      <td>0.729167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(entropy, 4, balanced)</td>\n",
       "      <td>0.729167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(gini, 6, balanced)</td>\n",
       "      <td>0.729167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           parameter_list  accuracy\n",
       "0      (entropy, 4, None)  0.781250\n",
       "1         (gini, 4, None)  0.760417\n",
       "2      (entropy, 6, None)  0.729167\n",
       "3  (entropy, 4, balanced)  0.729167\n",
       "4     (gini, 6, balanced)  0.729167"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "racc.reset_index(drop=True)# 将前面的序列变成0，1，2，3，4，5 方便查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=8)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=8)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', max_depth=8)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练模型\n",
    "model_DF=DecisionTreeClassifier(criterion='gini',max_depth=8,class_weight='balanced')\n",
    "model_DF.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.47671685e-01, 1.92519579e-01, 6.91834543e-02, 5.71930991e-02,\n",
       "       4.27141152e-02, 4.55045126e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 6.99024501e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 6.03642785e-03, 0.00000000e+00,\n",
       "       9.98489693e-03, 0.00000000e+00, 5.62114567e-03, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.29636168e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.01686896e-02,\n",
       "       2.31992181e-18, 0.00000000e+00, 6.49470209e-03, 0.00000000e+00,\n",
       "       1.23281620e-03, 3.74372871e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 4.00506629e-03,\n",
       "       4.97042747e-04, 0.00000000e+00, 1.14565155e-02, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.04307756e-02, 0.00000000e+00, 1.22833191e-02,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.31823637e-02,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       3.15031205e-02, 0.00000000e+00, 8.84663993e-03, 3.94386708e-17,\n",
       "       0.00000000e+00, 0.00000000e+00, 3.10726504e-01, 0.00000000e+00])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_DF.feature_importances_ # 特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StudentAbsenceDays_Above-7    0.310727\n",
       "VisitedResources              0.192520\n",
       "Raisedhands                   0.147672\n",
       "AnnouncementsView             0.069183\n",
       "Discussion                    0.057193\n",
       "                                ...   \n",
       "GradeID_G-09                  0.000000\n",
       "GradeID_G-10                  0.000000\n",
       "GradeID_G-11                  0.000000\n",
       "GradeID_G-12                  0.000000\n",
       "StudentAbsenceDays_Under-7    0.000000\n",
       "Length: 72, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征重要性\n",
    "fea_imp=pd.Series(model_DF.feature_importances_,index=X_train.columns).sort_values(ascending=False)[:]  # 将特征重要性排序后输出\n",
    "fea_imp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 使用PCA进行降维，在此不清楚很好的具体哪些特征对成绩的影响程度最大,为了进行可视化分析，在此进行多种展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA  # 导入，将在sklearn使用PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Nationality', 'PlaceofBirth', 'StageID', 'GradeID',\n",
       "       'SectionID', 'Topic', 'Semester', 'Relation', 'Raisedhands',\n",
       "       'VisitedResources', 'AnnouncementsView', 'Discussion',\n",
       "       'ParentAnsweringSurvey', 'ParentschoolSatisfaction',\n",
       "       'StudentAbsenceDays', 'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('Class',axis=1)\n",
    "y_new=df['Class']\n",
    "X_new=pd.get_dummies(X) # 归一化之后数据，列的数量太大，不方便进行测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raisedhands</th>\n",
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       "      <th>Discussion</th>\n",
       "      <th>Gender_F</th>\n",
       "      <th>Gender_M</th>\n",
       "      <th>Nationality_Egypt</th>\n",
       "      <th>Nationality_Iran</th>\n",
       "      <th>Nationality_Iraq</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Raisedhands  VisitedResources  AnnouncementsView  Discussion  Gender_F  \\\n",
       "0             15                16                  2          20         0   \n",
       "1             20                20                  3          25         0   \n",
       "2             10                 7                  0          30         0   \n",
       "3             30                25                  5          35         0   \n",
       "4             40                50                 12          50         0   \n",
       "..           ...               ...                ...         ...       ...   \n",
       "475            5                 4                  5           8         1   \n",
       "476           50                77                 14          28         1   \n",
       "477           55                74                 25          29         1   \n",
       "478           30                17                 14          57         1   \n",
       "479           35                14                 23          62         1   \n",
       "\n",
       "     Gender_M  Nationality_Egypt  Nationality_Iran  Nationality_Iraq  \\\n",
       "0           1                  0                 0                 0   \n",
       "1           1                  0                 0                 0   \n",
       "2           1                  0                 0                 0   \n",
       "3           1                  0                 0                 0   \n",
       "4           1                  0                 0                 0   \n",
       "..        ...                ...               ...               ...   \n",
       "475         0                  0                 0                 0   \n",
       "476         0                  0                 0                 0   \n",
       "477         0                  0                 0                 0   \n",
       "478         0                  0                 0                 0   \n",
       "479         0                  0                 0                 0   \n",
       "\n",
       "     Nationality_Jordan  ...  Semester_F  Semester_S  Relation_Father  \\\n",
       "0                     0  ...           1           0                1   \n",
       "1                     0  ...           1           0                1   \n",
       "2                     0  ...           1           0                1   \n",
       "3                     0  ...           1           0                1   \n",
       "4                     0  ...           1           0                1   \n",
       "..                  ...  ...         ...         ...              ...   \n",
       "475                   1  ...           0           1                1   \n",
       "476                   1  ...           1           0                1   \n",
       "477                   1  ...           0           1                1   \n",
       "478                   1  ...           1           0                1   \n",
       "479                   1  ...           0           1                1   \n",
       "\n",
       "     Relation_Mum  ParentAnsweringSurvey_No  ParentAnsweringSurvey_Yes  \\\n",
       "0               0                         0                          1   \n",
       "1               0                         0                          1   \n",
       "2               0                         1                          0   \n",
       "3               0                         1                          0   \n",
       "4               0                         1                          0   \n",
       "..            ...                       ...                        ...   \n",
       "475             0                         1                          0   \n",
       "476             0                         1                          0   \n",
       "477             0                         1                          0   \n",
       "478             0                         1                          0   \n",
       "479             0                         1                          0   \n",
       "\n",
       "     ParentschoolSatisfaction_Bad  ParentschoolSatisfaction_Good  \\\n",
       "0                               0                              1   \n",
       "1                               0                              1   \n",
       "2                               1                              0   \n",
       "3                               1                              0   \n",
       "4                               1                              0   \n",
       "..                            ...                            ...   \n",
       "475                             1                              0   \n",
       "476                             1                              0   \n",
       "477                             1                              0   \n",
       "478                             1                              0   \n",
       "479                             1                              0   \n",
       "\n",
       "     StudentAbsenceDays_Above-7  StudentAbsenceDays_Under-7  \n",
       "0                             0                           1  \n",
       "1                             0                           1  \n",
       "2                             1                           0  \n",
       "3                             1                           0  \n",
       "4                             1                           0  \n",
       "..                          ...                         ...  \n",
       "475                           1                           0  \n",
       "476                           0                           1  \n",
       "477                           0                           1  \n",
       "478                           1                           0  \n",
       "479                           1                           0  \n",
       "\n",
       "[480 rows x 72 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new # 输出看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca2 = PCA(n_components=2,random_state=2021)\n",
    "feat2 = pca2.fit_transform(X_new.values)\n",
    "dp = pd.DataFrame({\"feat1\":feat2[:,1:].reshape(len(feat2),).tolist(),\"feat2\":feat2[:,:1].reshape(len(feat2),).tolist(),\"color\":y_new})\n",
    "sns.scatterplot(data=dp,x=\"feat1\",y=\"feat2\",hue=\"color\")\n",
    "plt.title(\"PCA\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0.8 表示满足0.8密度信息的最少维度的数据\n",
    "pca08 = PCA(n_components=0.8,random_state=2021)\n",
    "feat08 = pca08.fit_transform(X_new.values)\n",
    "showd = pca2.fit_transform(feat08)\n",
    "dp08 = pd.DataFrame({\"feat1\":showd[:,1:].reshape(len(showd),).tolist(),\"feat2\":showd[:,:1].reshape(len(showd),).tolist(),\"color\":y_new})\n",
    "sns.scatterplot(data=dp08,x=\"feat1\",y=\"feat2\",hue=\"color\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Relation</th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>VisitedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>77</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>74</td>\n",
       "      <td>25</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Relation  Raisedhands  VisitedResources  AnnouncementsView  \\\n",
       "0         1         1           15                16                  2   \n",
       "1         1         1           20                20                  3   \n",
       "2         1         1           10                 7                  0   \n",
       "3         1         1           30                25                  5   \n",
       "4         1         1           40                50                 12   \n",
       "..      ...       ...          ...               ...                ...   \n",
       "475       0         1            5                 4                  5   \n",
       "476       0         1           50                77                 14   \n",
       "477       0         1           55                74                 25   \n",
       "478       0         1           30                17                 14   \n",
       "479       0         1           35                14                 23   \n",
       "\n",
       "     Discussion  StudentAbsenceDays  \n",
       "0            20                   0  \n",
       "1            25                   0  \n",
       "2            30                   1  \n",
       "3            35                   1  \n",
       "4            50                   1  \n",
       "..          ...                 ...  \n",
       "475           8                   1  \n",
       "476          28                   0  \n",
       "477          29                   0  \n",
       "478          57                   1  \n",
       "479          62                   1  \n",
       "\n",
       "[480 rows x 7 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(df,columns=['Gender','Relation','Raisedhands','VisitedResources','AnnouncementsView','Discussion','StudentAbsenceDays'])\n",
    "replaceMap = {'M':1,'F':0,'Father':1,'Mum':0,'Under-7':0,'Above-7':1}\n",
    "# X = X.map(replaceMap)\n",
    "X['Gender'] = X['Gender'].map(replaceMap)\n",
    "X['Relation'] = X['Relation'].map(replaceMap)\n",
    "X['StudentAbsenceDays'] = X['StudentAbsenceDays'].map(replaceMap)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_t = pca2.fit_transform(X)\n",
    "dp = pd.DataFrame({\"feat1\":feat_t[:,1:].reshape(len(feat_t),).tolist(),\"feat2\":feat_t[:,:1].reshape(len(feat_t),).tolist(),\"color\":y_new})\n",
    "sns.scatterplot(data=dp,x=\"feat1\",y=\"feat2\",hue=\"color\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Relation</th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>VisitedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Relation  Raisedhands  VisitedResources  AnnouncementsView  \\\n",
       "0       1         1           15                16                  2   \n",
       "1       1         1           20                20                  3   \n",
       "2       1         1           10                 7                  0   \n",
       "3       1         1           30                25                  5   \n",
       "4       1         1           40                50                 12   \n",
       "5       0         1           42                30                 13   \n",
       "6       1         1           35                12                  0   \n",
       "7       1         1           50                10                 15   \n",
       "8       0         1           12                21                 16   \n",
       "9       0         1           70                80                 25   \n",
       "\n",
       "   Discussion  StudentAbsenceDays  \n",
       "0          20                   0  \n",
       "1          25                   0  \n",
       "2          30                   1  \n",
       "3          35                   1  \n",
       "4          50                   1  \n",
       "5          70                   1  \n",
       "6          17                   1  \n",
       "7          22                   0  \n",
       "8          50                   0  \n",
       "9          70                   0  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)   #  二维表格类型，DataFrame对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 7)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape # 输出行、列数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Relation', 'Raisedhands', 'VisitedResources',\n",
       "       'AnnouncementsView', 'Discussion', 'StudentAbsenceDays'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns  # 输出行序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 480 entries, 0 to 479\n",
      "Data columns (total 8 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   Gender              480 non-null    int64 \n",
      " 1   Relation            480 non-null    int64 \n",
      " 2   Raisedhands         480 non-null    int64 \n",
      " 3   VisitedResources    480 non-null    int64 \n",
      " 4   AnnouncementsView   480 non-null    int64 \n",
      " 5   Discussion          480 non-null    int64 \n",
      " 6   StudentAbsenceDays  480 non-null    int64 \n",
      " 7   Class               480 non-null    object\n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 49.9+ KB\n"
     ]
    }
   ],
   "source": [
    "X.info()   # info看具体情况，25个数据 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Relation</th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>VisitedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.635417</td>\n",
       "      <td>0.589583</td>\n",
       "      <td>46.775000</td>\n",
       "      <td>54.797917</td>\n",
       "      <td>37.918750</td>\n",
       "      <td>43.283333</td>\n",
       "      <td>0.397917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.481815</td>\n",
       "      <td>0.492423</td>\n",
       "      <td>30.779223</td>\n",
       "      <td>33.080007</td>\n",
       "      <td>26.611244</td>\n",
       "      <td>27.637735</td>\n",
       "      <td>0.489979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gender    Relation  Raisedhands  VisitedResources  \\\n",
       "count  480.000000  480.000000   480.000000        480.000000   \n",
       "mean     0.635417    0.589583    46.775000         54.797917   \n",
       "std      0.481815    0.492423    30.779223         33.080007   \n",
       "min      0.000000    0.000000     0.000000          0.000000   \n",
       "25%      0.000000    0.000000    15.750000         20.000000   \n",
       "50%      1.000000    1.000000    50.000000         65.000000   \n",
       "75%      1.000000    1.000000    75.000000         84.000000   \n",
       "max      1.000000    1.000000   100.000000         99.000000   \n",
       "\n",
       "       AnnouncementsView  Discussion  StudentAbsenceDays  \n",
       "count         480.000000  480.000000          480.000000  \n",
       "mean           37.918750   43.283333            0.397917  \n",
       "std            26.611244   27.637735            0.489979  \n",
       "min             0.000000    1.000000            0.000000  \n",
       "25%            14.000000   20.000000            0.000000  \n",
       "50%            33.000000   39.000000            0.000000  \n",
       "75%            58.000000   70.000000            1.000000  \n",
       "max            98.000000   99.000000            1.000000  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()   #  个数、平均数、标准差、最小值、下次分位数、中位数、上次分位数、最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  1 15 ...  2 20  0]\n",
      " [ 1  1 20 ...  3 25  0]\n",
      " [ 1  1 10 ...  0 30  1]\n",
      " ...\n",
      " [ 0  1 55 ... 25 29  0]\n",
      " [ 0  1 30 ... 14 57  1]\n",
      " [ 0  1 35 ... 23 62  1]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(480, 7)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_array = np.array(X)\n",
    "\n",
    "print(data_array) \n",
    "data_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-65.27693828  -5.43625802]\n",
      " [-58.02761959  -3.09856142]\n",
      " [-71.60497143   7.57912425]\n",
      " [-45.36149175   2.80738553]\n",
      " [-16.73642407   5.61358089]\n",
      " [-21.39924752  31.58418862]\n",
      " [-58.01462499  -9.22211023]\n",
      " [-42.23613353  -3.93990333]\n",
      " [-48.66917601  21.47785901]\n",
      " [ 30.67956211   9.09593204]\n",
      " [ 29.29119359  17.88244007]\n",
      " [-63.62820716  -6.89309797]\n",
      " [-83.74914069  -6.34728749]\n",
      " [-59.32966846  -4.8948074 ]\n",
      " [ 25.74932033   7.59053692]\n",
      " [-19.2591222   26.51615855]\n",
      " [-18.68714612  42.15081223]\n",
      " [ -8.27195281  57.55670574]\n",
      " [  3.24403856  60.48206862]\n",
      " [ 27.85837133  49.20016571]\n",
      " [ 22.31733534  37.43118787]\n",
      " [-51.94702055  50.78161383]\n",
      " [-41.55370993  55.17511751]\n",
      " [-73.64621494  29.59450799]\n",
      " [-67.21197121  47.05112745]\n",
      " [-56.0345797   20.49656447]\n",
      " [-44.68522982  13.58845948]\n",
      " [-51.70803861   6.60424943]\n",
      " [ 41.01512459 -14.39915494]\n",
      " [  7.08724504 -20.35371969]\n",
      " [ 22.01336723  16.55576421]\n",
      " [-62.05479978   1.5700657 ]\n",
      " [-58.3999879   22.80182848]\n",
      " [-56.53495297  11.74981189]\n",
      " [-61.39251274  15.6336909 ]\n",
      " [-60.75797591   7.63695216]\n",
      " [-73.88847282   1.56920164]\n",
      " [  6.82452393  40.89005392]\n",
      " [-89.30300403 -11.58037287]\n",
      " [ 30.76964536   8.97883318]\n",
      " [-52.45435201  40.79964181]\n",
      " [-45.25233498  21.01514993]\n",
      " [-64.46086802   9.08344678]\n",
      " [-12.46175374  50.79118126]\n",
      " [-11.03597302  51.48437024]\n",
      " [-44.10601452  41.94828243]\n",
      " [-71.04491616   6.84794398]\n",
      " [ -3.27645191  59.8646146 ]\n",
      " [ 10.08660852  34.16816474]\n",
      " [ -4.34486664  40.36597736]\n",
      " [-44.26268835  40.75714163]\n",
      " [-36.79513316  20.5721662 ]\n",
      " [ 15.14770983 -21.10028405]\n",
      " [ 11.23256686  19.53913148]\n",
      " [-40.5871353   -9.29807723]\n",
      " [-64.08930724  -4.24444704]\n",
      " [-74.37545536 -18.18678895]\n",
      " [-82.15895266 -11.38443493]\n",
      " [-14.33253344   3.87832158]\n",
      " [-39.6296163   10.99050164]\n",
      " [-53.54704296  -0.5068312 ]\n",
      " [  7.42917255 -10.30130636]\n",
      " [ 66.13157056  18.27614553]\n",
      " [-61.89193548  -9.88316177]\n",
      " [-83.6358767  -14.55174423]\n",
      " [-66.08601366   0.40806139]\n",
      " [-52.425609     6.8342886 ]\n",
      " [ 26.79708675  20.46775659]\n",
      " [ 16.75652749 -20.96768777]\n",
      " [-28.87840244  -5.50789923]\n",
      " [-51.41865416 -18.2125353 ]\n",
      " [-50.35435215  -8.77203078]\n",
      " [-79.65084554  -9.94399956]\n",
      " [-43.73272961  -9.59048684]\n",
      " [-76.98106687  -4.27804504]\n",
      " [ 24.14654242  -0.96689945]\n",
      " [ -9.22966141  -1.74209867]\n",
      " [ -5.35046458 -16.80075402]\n",
      " [-73.47054557  -8.59123599]\n",
      " [ 49.11145108  -6.70418823]\n",
      " [-60.33466231  14.7398283 ]\n",
      " [-49.51702073  36.3838345 ]\n",
      " [-58.86181099  53.7771141 ]\n",
      " [-64.66151789  28.3237559 ]\n",
      " [  6.8224845  -34.9238603 ]\n",
      " [-85.90553771  -4.42001412]\n",
      " [ -9.7662111   20.90907807]\n",
      " [-20.76250269  -1.07883826]\n",
      " [-69.85837348   9.5825968 ]\n",
      " [-60.19667886  20.90332888]\n",
      " [-60.99730559  36.01040345]\n",
      " [ 41.22491028 -38.18236639]\n",
      " [ -5.91968473 -34.29050275]\n",
      " [ 25.5505596  -17.71707545]\n",
      " [  1.36566776 -29.87031839]\n",
      " [ 37.38658835   2.50710249]\n",
      " [-18.67264682  27.01053615]\n",
      " [-64.82374481  -1.28023671]\n",
      " [-74.03442638   9.21611639]\n",
      " [-38.55102373  21.61118321]\n",
      " [ 38.05244882  27.25341397]\n",
      " [ 30.85564553 -49.11985651]\n",
      " [-82.39631379 -10.45945621]\n",
      " [-79.93800059  -8.45850542]\n",
      " [-80.91232391 -17.01984873]\n",
      " [-64.55660469  57.06950649]\n",
      " [-52.70773479   9.36310269]\n",
      " [ 33.18176608 -26.13847734]\n",
      " [-75.96673051 -17.64456996]\n",
      " [ 21.84695485 -38.80844512]\n",
      " [ 40.46177582 -17.32214475]\n",
      " [ 49.59107009  20.39613939]\n",
      " [-70.14174383  27.67006117]\n",
      " [-39.27135217  32.42652583]\n",
      " [-68.16723364  27.40559517]\n",
      " [-59.88603301  45.58425803]\n",
      " [ 13.67514694 -39.04103529]\n",
      " [-35.04643573  -9.98935465]\n",
      " [-38.29678421  -9.95164044]\n",
      " [ -0.30840695   6.85883771]\n",
      " [-56.31303149  22.05759411]\n",
      " [-75.51108002  11.04236539]\n",
      " [ 31.09824069 -42.70190502]\n",
      " [ 14.18256122 -58.69216805]\n",
      " [-80.55817802  -3.69783477]\n",
      " [-60.28472419 -13.72932657]\n",
      " [-64.38569028  31.0009735 ]\n",
      " [-33.32503732 -26.73576429]\n",
      " [-63.5790457   -3.9548163 ]\n",
      " [  6.1271112  -58.71957416]\n",
      " [-69.81657356   7.75305177]\n",
      " [  5.08298178   4.89476414]\n",
      " [-83.1819835  -12.46559334]\n",
      " [-26.3553978   21.42299473]\n",
      " [ 31.84262855   6.61070249]\n",
      " [ 62.80704191  17.76504785]\n",
      " [ 62.80704191  17.76504785]\n",
      " [ 27.54036725 -13.41782667]\n",
      " [ 85.97685128  31.92851381]\n",
      " [ 78.52360841  10.73860622]\n",
      " [-39.98141333 -22.46519605]\n",
      " [-14.06038074 -29.01156803]\n",
      " [-10.55271994 -30.47340386]\n",
      " [ 41.22491028 -38.18236639]\n",
      " [-66.14336416  -1.63802315]\n",
      " [ 23.9710902  -37.95202309]\n",
      " [ 56.37253378  10.49892953]\n",
      " [-13.97463041 -18.20187122]\n",
      " [  3.1304595   -5.65063895]\n",
      " [ 56.37253378  10.49892953]\n",
      " [ 45.11396109   1.22429407]\n",
      " [ 20.00143805  44.62534173]\n",
      " [ 79.28011765 -16.95975825]\n",
      " [-23.49375092   3.46081571]\n",
      " [  7.50398735  -3.58024215]\n",
      " [ 45.50813197  42.85349089]\n",
      " [  8.49742145   3.20751252]\n",
      " [  7.21376369  -4.47693129]\n",
      " [-21.77958313 -23.89793414]\n",
      " [ 32.27586638  26.81000259]\n",
      " [ 52.30632598  -9.9044721 ]\n",
      " [ 45.79484289   9.35132379]\n",
      " [ 22.69193759   8.23304886]\n",
      " [ 49.372641    -6.22283222]\n",
      " [ 27.24716701 -28.70075357]\n",
      " [ 43.54359367  17.35459465]\n",
      " [ 25.84110587 -10.05814784]\n",
      " [ 62.38597403 -14.05272667]\n",
      " [ -3.97488803 -11.55805967]\n",
      " [-45.80180529   0.35992716]\n",
      " [ 18.13883287  -5.72499816]\n",
      " [ -3.97488803 -11.55805967]\n",
      " [-37.40998407  -8.46673962]\n",
      " [-26.99593301  -6.57076315]\n",
      " [ 22.25432823   0.74245305]\n",
      " [-61.89064375  31.82625338]\n",
      " [ -3.97488803 -11.55805967]\n",
      " [ -8.8301497  -18.20874681]\n",
      " [  0.12491963 -26.61823732]\n",
      " [-17.46866073  -9.0271953 ]\n",
      " [ 43.06705246  39.73514099]\n",
      " [ -8.23591457 -15.64233631]\n",
      " [ 48.22493173  -1.36194015]\n",
      " [-36.6149831   26.98318938]\n",
      " [-45.80036658 -15.39550441]\n",
      " [ 46.55484994 -12.93948163]\n",
      " [ -6.67813931  18.26244096]\n",
      " [ 37.7295173   -2.12604248]\n",
      " [ 44.62359046   2.76208929]\n",
      " [ 10.93356114 -30.72049441]\n",
      " [-31.67647426  23.13592739]\n",
      " [-45.80609021 -11.05146332]\n",
      " [ 63.72470277  -9.16306148]\n",
      " [ 45.92334439  -0.86336554]\n",
      " [ 25.5394543  -22.98858489]\n",
      " [-26.63444748   0.09520236]\n",
      " [ 46.16158291   1.86223226]\n",
      " [ 47.07413806  -1.11265285]\n",
      " [-30.88210739  27.97450283]\n",
      " [  2.62617876 -46.56637353]\n",
      " [ 21.35662193  26.83334029]\n",
      " [  7.31293722   0.3977204 ]\n",
      " [-57.79399373  10.69937707]\n",
      " [-15.66417763   2.21119715]\n",
      " [-55.11057153   0.0977648 ]\n",
      " [  8.37810473 -18.34499181]\n",
      " [ -9.6356731  -18.47789085]\n",
      " [-44.49664156  -8.14903016]\n",
      " [-23.34412987 -16.72491365]\n",
      " [ 41.84709247  22.95282624]\n",
      " [ 21.6463474  -32.71019417]\n",
      " [-20.13213487 -24.53574213]\n",
      " [ 33.80880861 -36.98179616]\n",
      " [-35.06943028 -25.76559865]\n",
      " [-48.14780891 -33.93492061]\n",
      " [-51.40657515  -6.65175595]\n",
      " [-17.26715852  23.24199241]\n",
      " [ 34.4941134  -48.15029664]\n",
      " [ 48.95976788  37.22517536]\n",
      " [  4.54008284  -7.0298022 ]\n",
      " [ 18.51027362 -36.31073471]\n",
      " [ 12.0628757  -19.58801089]\n",
      " [ 31.79730932  -1.91541147]\n",
      " [ 33.6019084   23.90661122]\n",
      " [ 39.69580906   6.19243814]\n",
      " [ 20.14198419 -32.66939011]\n",
      " [-45.31424184  14.61736653]\n",
      " [-81.90332346  -7.81552788]\n",
      " [ 61.95169479  23.37059223]\n",
      " [-64.66166995 -11.19537546]\n",
      " [-21.47932416 -11.28140743]\n",
      " [-65.49725713   1.3541157 ]\n",
      " [-14.04255561  -0.40991745]\n",
      " [ 16.86771778  -3.56428396]\n",
      " [ 10.59154457  22.53197629]\n",
      " [-62.766306     5.5696731 ]\n",
      " [-46.7346504  -18.75764509]\n",
      " [ 45.78573578   9.35621031]\n",
      " [ 45.7914955    9.35274546]\n",
      " [ 74.023965    19.52660363]\n",
      " [ 73.01301033  41.161174  ]\n",
      " [ 51.69881163  27.25743553]\n",
      " [-55.61088992 -18.49555261]\n",
      " [-40.3708942   21.03427851]\n",
      " [ 69.03860468  24.78953858]\n",
      " [  6.01604776  27.7400506 ]\n",
      " [ 64.00846198  12.96412809]\n",
      " [ 54.80255016  50.42337186]\n",
      " [-20.19633655 -25.70281962]\n",
      " [ 39.56798946 -15.00733047]\n",
      " [-51.69292742  20.27996068]\n",
      " [-14.76700462 -28.66600201]\n",
      " [ 60.91494292  39.40443134]\n",
      " [-33.70289621  10.19941905]\n",
      " [ 42.50472027   5.60895525]\n",
      " [-57.04782775  13.91898974]\n",
      " [ 59.31596797   1.85002899]\n",
      " [ 59.59094549   1.91612897]\n",
      " [ 82.16670045  30.82103482]\n",
      " [-19.53982503 -20.98931616]\n",
      " [-64.73327489  -9.35306816]\n",
      " [ 60.2342821    4.94607247]\n",
      " [-28.04280801   8.03417122]\n",
      " [ 16.3122794  -48.31113369]\n",
      " [ 17.51410901 -16.26182602]\n",
      " [ 54.70822095 -16.14559416]\n",
      " [-20.29441968 -33.59851606]\n",
      " [-18.26582296 -15.28518007]\n",
      " [-72.3146853   -9.49439742]\n",
      " [-38.80807187  -6.01674972]\n",
      " [ 45.20258639 -27.49941094]\n",
      " [ 37.12025785 -23.62670559]\n",
      " [-47.87724124 -14.43053079]\n",
      " [-55.20470738  -2.08103344]\n",
      " [ 72.44438707  12.21902268]\n",
      " [ 53.51338185  11.8364248 ]\n",
      " [-18.47929296  -6.74394536]\n",
      " [  8.59332114  -9.96051424]\n",
      " [  6.8376401   -9.26543354]\n",
      " [  5.0121422  -11.06875047]\n",
      " [ 31.97934579 -13.41776225]\n",
      " [ 25.27910645  -8.91141646]\n",
      " [ 78.11861347  15.01547517]\n",
      " [ 81.36706751  24.24361684]\n",
      " [-19.00315358   0.475495  ]\n",
      " [-15.55573511   2.99827639]\n",
      " [ 52.2888207   36.31166017]\n",
      " [ 71.12259156  29.73514846]\n",
      " [ 43.22685945  17.12530697]\n",
      " [ 54.43489235  27.42640501]\n",
      " [-12.67782152   7.33401925]\n",
      " [-39.10101117  -7.25808058]\n",
      " [ 73.18543442  14.78412487]\n",
      " [ 42.03871873  34.99930484]\n",
      " [ 73.17450314  14.78914008]\n",
      " [ 42.02778746  35.00432006]\n",
      " [ 83.18730502  22.82426431]\n",
      " [ 59.94986758  35.37080592]\n",
      " [-47.79748706   9.28704858]\n",
      " [-40.29279878   5.03869996]\n",
      " [-64.40297124  -7.20873057]\n",
      " [-47.57385026 -21.91716197]\n",
      " [-55.53088187  -1.54045187]\n",
      " [-33.77482648   3.32066644]\n",
      " [-27.67522245   3.94872779]\n",
      " [ -5.6654335   46.08363715]\n",
      " [ 41.33994208  36.02648713]\n",
      " [ 43.30203793  36.61809267]\n",
      " [ 59.15082155  38.47889614]\n",
      " [ 67.45729143  20.70630613]\n",
      " [-33.08449021   3.594528  ]\n",
      " [-20.77934826   4.17861871]\n",
      " [ 22.18700939 -10.44287729]\n",
      " [ 30.3231629    3.29777107]\n",
      " [ 66.48619245  22.52562476]\n",
      " [ 69.25334868  32.14137312]\n",
      " [ 44.97013302 -29.46262917]\n",
      " [ 35.04398718 -36.34824482]\n",
      " [  9.98607198 -58.08632441]\n",
      " [  9.67072254 -48.51565911]\n",
      " [ 10.84201322 -45.58806412]\n",
      " [  0.62911372 -38.26507438]\n",
      " [-64.80154927  -2.51554545]\n",
      " [-56.47642157 -16.04337828]\n",
      " [-37.93335432  -2.51293779]\n",
      " [-37.27865634   8.69708592]\n",
      " [-64.80154927  -2.51554545]\n",
      " [-56.47642157 -16.04337828]\n",
      " [ 55.88876126  25.41986936]\n",
      " [ 61.58757053  21.31925061]\n",
      " [-28.14833024  19.88094975]\n",
      " [-46.07110275  12.78966119]\n",
      " [ -9.92901244 -30.76780219]\n",
      " [ -7.25730393 -26.87439362]\n",
      " [-74.83742402 -15.88759721]\n",
      " [-70.01124832 -12.14891605]\n",
      " [ 20.00571995 -39.48534314]\n",
      " [ 14.20164337 -37.52648305]\n",
      " [ 21.93483052 -53.9468081 ]\n",
      " [ 18.37330677 -47.57513305]\n",
      " [-16.31813576 -42.3220983 ]\n",
      " [-23.04707636 -39.49298708]\n",
      " [ 20.58979026 -12.89736072]\n",
      " [  8.75814443 -24.08532727]\n",
      " [-12.31501956 -42.25840542]\n",
      " [-15.00624409 -21.04706345]\n",
      " [ -6.5610512  -43.50484196]\n",
      " [ -9.25227574 -22.29349999]\n",
      " [-72.129019   -15.08569475]\n",
      " [-71.83858632 -10.91991265]\n",
      " [-75.15938254 -14.99897811]\n",
      " [-72.9689198   -9.40957825]\n",
      " [ -5.85743617 -40.11893946]\n",
      " [ -8.40050427 -40.50392897]\n",
      " [ 11.95689475 -35.05118292]\n",
      " [ 14.35664892 -33.598348  ]\n",
      " [  3.6051293  -28.85882266]\n",
      " [ -5.48895632 -27.14918561]\n",
      " [ 38.94209247 -44.41825604]\n",
      " [ 35.92726429 -39.84845922]\n",
      " [ -2.87752642 -24.80002429]\n",
      " [  5.0026618  -17.45995625]\n",
      " [ 51.03958021 -27.43299892]\n",
      " [ 44.07895814 -19.9558239 ]\n",
      " [ 31.3261662  -36.38679019]\n",
      " [ 19.83202881 -23.76174741]\n",
      " [-29.95911834  19.03023283]\n",
      " [-11.818815     9.11706523]\n",
      " [ 56.20886916 -28.24895522]\n",
      " [ 35.46696894 -20.94408978]\n",
      " [-45.22184138   1.61925544]\n",
      " [-49.01988334   3.83672304]\n",
      " [-33.79723216  64.95324011]\n",
      " [-44.45578379  70.60626644]\n",
      " [-45.21802292  30.10372205]\n",
      " [-49.0250549    3.8382734 ]\n",
      " [ 16.81192428   6.2708349 ]\n",
      " [ 23.56474538  10.95776054]\n",
      " [-16.63088324  58.95483893]\n",
      " [-18.58598265  55.93681332]\n",
      " [-33.84425566  65.4759509 ]\n",
      " [-36.63935994  65.51727259]\n",
      " [ 16.62336412 -29.81725694]\n",
      " [ 23.41501967 -24.76084496]\n",
      " [ 43.40240717 -33.92909329]\n",
      " [ 40.91074538 -36.4529889 ]\n",
      " [ 11.26601391  35.5246259 ]\n",
      " [  8.15505423  33.40976172]\n",
      " [-74.97218329 -12.13073143]\n",
      " [-84.78159566  -8.50248002]\n",
      " [ 35.39356332 -44.17012979]\n",
      " [ 43.23456719 -45.00124545]\n",
      " [ 47.5568087  -41.93535764]\n",
      " [ 48.78863955 -36.49906711]\n",
      " [ 44.59592547 -20.16762954]\n",
      " [ 55.54481742   7.1018547 ]\n",
      " [ 43.41021265   2.39511078]\n",
      " [ 54.06201366   4.35343269]\n",
      " [ 78.3687293   20.15974597]\n",
      " [ 82.12986522  21.37772079]\n",
      " [  1.23947766 -20.87914915]\n",
      " [  0.48487682 -28.39149287]\n",
      " [ 64.70548269  29.07482088]\n",
      " [ 54.72491357  21.60572883]\n",
      " [ 75.43135528   5.28199158]\n",
      " [ 72.18077784  12.81140571]\n",
      " [-78.96336869 -16.96650116]\n",
      " [-74.27612412 -13.77616992]\n",
      " [  0.15665857 -24.77019333]\n",
      " [  7.49674574 -27.1647491 ]\n",
      " [ 15.72534508 -40.0668127 ]\n",
      " [ 19.7161417  -18.54510198]\n",
      " [ 48.48362233  -3.82914628]\n",
      " [ 59.60456577  29.57859161]\n",
      " [-78.02963204 -18.95491645]\n",
      " [-76.02010936 -15.35341445]\n",
      " [ 81.16874551   9.96192164]\n",
      " [ 82.12986522  21.37772079]\n",
      " [ 73.77965155  18.92082426]\n",
      " [ 90.61567946  19.38775183]\n",
      " [ 30.17313701 -31.37222705]\n",
      " [ 39.90836568 -21.24160721]\n",
      " [ 26.23026968  -6.17624185]\n",
      " [ 25.05400125  -5.22574292]\n",
      " [ 67.64517517  21.38414033]\n",
      " [ 61.10194478  13.4764878 ]\n",
      " [ 35.98174033 -40.38133924]\n",
      " [ 41.57850735 -38.66779574]\n",
      " [-71.70159719  20.54110975]\n",
      " [-64.33245887  27.62548593]\n",
      " [ 40.75542423  -4.88852979]\n",
      " [ 40.40511433   2.1245521 ]\n",
      " [ 69.55538255  17.54283169]\n",
      " [ 70.14901069  38.39749644]\n",
      " [ 47.41619571 -43.52844917]\n",
      " [ 50.73923393 -33.37162206]\n",
      " [ 46.3387036  -22.66989106]\n",
      " [ 39.2762863  -28.63265374]\n",
      " [ 45.99300778 -32.30662457]\n",
      " [ 42.73408379 -22.23269241]\n",
      " [ 57.62463953 -37.81838986]\n",
      " [ 55.65503985 -42.13295178]\n",
      " [ 53.15503971  21.2421166 ]\n",
      " [ 46.59636228  15.68863297]\n",
      " [ 37.42073637 -11.16910842]\n",
      " [ 39.34420729 -11.29092554]\n",
      " [ 61.0727146   21.52784879]\n",
      " [ 55.92159971  21.48426869]\n",
      " [ 75.87176177  25.13612204]\n",
      " [ 67.74162189  25.95244776]\n",
      " [ -9.71278471 -45.09302283]\n",
      " [-10.08306197 -53.41015981]\n",
      " [  3.62050747 -27.5247391 ]\n",
      " [ -4.38441087 -35.0324588 ]\n",
      " [ 38.19743083  22.26028606]\n",
      " [ 26.56629192  13.70287956]\n",
      " [ 45.97634266 -29.3308633 ]\n",
      " [ 39.39318927 -36.52545592]\n",
      " [ 77.37518899  18.75743784]\n",
      " [ 71.60289839  22.20381974]\n",
      " [ 64.70701114  14.57916368]\n",
      " [ 59.06451728  17.53932312]\n",
      " [ 63.26832324  28.81121803]\n",
      " [ 61.38517723  37.3300572 ]\n",
      " [ 64.59692735  19.44819537]\n",
      " [ 67.03869044  22.34057822]\n",
      " [ 52.00835201   1.11695306]\n",
      " [ 62.86825185  -2.59551292]\n",
      " [-46.31346817  52.62787103]\n",
      " [-49.65730334  59.20432241]\n",
      " [ 60.0886922  -13.13462841]\n",
      " [ 55.79953139  -1.37149827]\n",
      " [ 62.59118552   9.26238842]\n",
      " [ 68.96261167  10.63924077]\n",
      " [-80.86938557 -10.62133967]\n",
      " [-80.51162529  -9.64237506]\n",
      " [ -0.11029715 -26.14083408]\n",
      " [  6.28272977 -23.27709629]\n",
      " [-39.60370423  26.89268191]\n",
      " [-32.94691201  33.08919047]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# 使用sklearn中的PCA\n",
    "pca = PCA(n_components=2)   # 设置保留下来的特征个数为1\n",
    "z2_data = pca.fit_transform(data_array) # 用data_array训练PCA模型，计算，同时返回降维后的数据.\n",
    "\n",
    "print(z2_data)  # 输出数据，与前方的计算结果比较（sklearn会有取反的操作，不影响）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.77296397]\n",
      "[[-6.52769383]\n",
      " [-5.80276196]\n",
      " [-7.16049714]\n",
      " [-4.53614918]\n",
      " [-1.67364241]\n",
      " [-2.13992475]\n",
      " [-5.8014625 ]\n",
      " [-4.22361335]\n",
      " [-4.8669176 ]\n",
      " [ 3.06795621]\n",
      " [ 2.92911936]\n",
      " [-6.36282072]\n",
      " [-8.37491407]\n",
      " [-5.93296685]\n",
      " [ 2.57493203]\n",
      " [-1.92591222]\n",
      " [-1.86871461]\n",
      " [-0.82719528]\n",
      " [ 0.32440386]\n",
      " [ 2.78583713]\n",
      " [ 2.23173353]\n",
      " [-5.19470205]\n",
      " [-4.15537099]\n",
      " [-7.36462149]\n",
      " [-6.72119712]\n",
      " [-5.60345797]\n",
      " [-4.46852298]\n",
      " [-5.17080386]\n",
      " [ 4.10151246]\n",
      " [ 0.7087245 ]\n",
      " [ 2.20133672]\n",
      " [-6.20547998]\n",
      " [-5.83999879]\n",
      " [-5.6534953 ]\n",
      " [-6.13925127]\n",
      " [-6.07579759]\n",
      " [-7.38884728]\n",
      " [ 0.68245239]\n",
      " [-8.9303004 ]\n",
      " [ 3.07696454]\n",
      " [-5.2454352 ]\n",
      " [-4.5252335 ]\n",
      " [-6.4460868 ]\n",
      " [-1.24617537]\n",
      " [-1.1035973 ]\n",
      " [-4.41060145]\n",
      " [-7.10449162]\n",
      " [-0.32764519]\n",
      " [ 1.00866085]\n",
      " [-0.43448666]\n",
      " [-4.42626884]\n",
      " [-3.67951332]\n",
      " [ 1.51477098]\n",
      " [ 1.12325669]\n",
      " [-4.05871353]\n",
      " [-6.40893072]\n",
      " [-7.43754554]\n",
      " [-8.21589527]\n",
      " [-1.43325334]\n",
      " [-3.96296163]\n",
      " [-5.3547043 ]\n",
      " [ 0.74291725]\n",
      " [ 6.61315706]\n",
      " [-6.18919355]\n",
      " [-8.36358767]\n",
      " [-6.60860137]\n",
      " [-5.2425609 ]\n",
      " [ 2.67970867]\n",
      " [ 1.67565275]\n",
      " [-2.88784024]\n",
      " [-5.14186542]\n",
      " [-5.03543522]\n",
      " [-7.96508455]\n",
      " [-4.37327296]\n",
      " [-7.69810669]\n",
      " [ 2.41465424]\n",
      " [-0.92296614]\n",
      " [-0.53504646]\n",
      " [-7.34705456]\n",
      " [ 4.91114511]\n",
      " [-6.03346623]\n",
      " [-4.95170207]\n",
      " [-5.8861811 ]\n",
      " [-6.46615179]\n",
      " [ 0.68224845]\n",
      " [-8.59055377]\n",
      " [-0.97662111]\n",
      " [-2.07625027]\n",
      " [-6.98583735]\n",
      " [-6.01966789]\n",
      " [-6.09973056]\n",
      " [ 4.12249103]\n",
      " [-0.59196847]\n",
      " [ 2.55505596]\n",
      " [ 0.13656678]\n",
      " [ 3.73865884]\n",
      " [-1.86726468]\n",
      " [-6.48237448]\n",
      " [-7.40344264]\n",
      " [-3.85510237]\n",
      " [ 3.80524488]\n",
      " [ 3.08556455]\n",
      " [-8.23963138]\n",
      " [-7.99380006]\n",
      " [-8.09123239]\n",
      " [-6.45566047]\n",
      " [-5.27077348]\n",
      " [ 3.31817661]\n",
      " [-7.59667305]\n",
      " [ 2.18469548]\n",
      " [ 4.04617758]\n",
      " [ 4.95910701]\n",
      " [-7.01417438]\n",
      " [-3.92713522]\n",
      " [-6.81672336]\n",
      " [-5.9886033 ]\n",
      " [ 1.36751469]\n",
      " [-3.50464357]\n",
      " [-3.82967842]\n",
      " [-0.0308407 ]\n",
      " [-5.63130315]\n",
      " [-7.551108  ]\n",
      " [ 3.10982407]\n",
      " [ 1.41825612]\n",
      " [-8.0558178 ]\n",
      " [-6.02847242]\n",
      " [-6.43856903]\n",
      " [-3.33250373]\n",
      " [-6.35790457]\n",
      " [ 0.61271112]\n",
      " [-6.98165736]\n",
      " [ 0.50829818]\n",
      " [-8.31819835]\n",
      " [-2.63553978]\n",
      " [ 3.18426286]\n",
      " [ 6.28070419]\n",
      " [ 6.28070419]\n",
      " [ 2.75403672]\n",
      " [ 8.59768513]\n",
      " [ 7.85236084]\n",
      " [-3.99814133]\n",
      " [-1.40603807]\n",
      " [-1.05527199]\n",
      " [ 4.12249103]\n",
      " [-6.61433642]\n",
      " [ 2.39710902]\n",
      " [ 5.63725338]\n",
      " [-1.39746304]\n",
      " [ 0.31304595]\n",
      " [ 5.63725338]\n",
      " [ 4.51139611]\n",
      " [ 2.0001438 ]\n",
      " [ 7.92801177]\n",
      " [-2.34937509]\n",
      " [ 0.75039873]\n",
      " [ 4.5508132 ]\n",
      " [ 0.84974215]\n",
      " [ 0.72137637]\n",
      " [-2.17795831]\n",
      " [ 3.22758664]\n",
      " [ 5.2306326 ]\n",
      " [ 4.57948429]\n",
      " [ 2.26919376]\n",
      " [ 4.9372641 ]\n",
      " [ 2.7247167 ]\n",
      " [ 4.35435937]\n",
      " [ 2.58411059]\n",
      " [ 6.2385974 ]\n",
      " [-0.3974888 ]\n",
      " [-4.58018053]\n",
      " [ 1.81388329]\n",
      " [-0.3974888 ]\n",
      " [-3.74099841]\n",
      " [-2.6995933 ]\n",
      " [ 2.22543282]\n",
      " [-6.18906438]\n",
      " [-0.3974888 ]\n",
      " [-0.88301497]\n",
      " [ 0.01249196]\n",
      " [-1.74686607]\n",
      " [ 4.30670525]\n",
      " [-0.82359146]\n",
      " [ 4.82249317]\n",
      " [-3.66149831]\n",
      " [-4.58003666]\n",
      " [ 4.65548499]\n",
      " [-0.66781393]\n",
      " [ 3.77295173]\n",
      " [ 4.46235905]\n",
      " [ 1.09335611]\n",
      " [-3.16764743]\n",
      " [-4.58060902]\n",
      " [ 6.37247028]\n",
      " [ 4.59233444]\n",
      " [ 2.55394543]\n",
      " [-2.66344475]\n",
      " [ 4.61615829]\n",
      " [ 4.70741381]\n",
      " [-3.08821074]\n",
      " [ 0.26261788]\n",
      " [ 2.13566219]\n",
      " [ 0.73129372]\n",
      " [-5.77939937]\n",
      " [-1.56641776]\n",
      " [-5.51105715]\n",
      " [ 0.83781047]\n",
      " [-0.96356731]\n",
      " [-4.44966416]\n",
      " [-2.33441299]\n",
      " [ 4.18470925]\n",
      " [ 2.16463474]\n",
      " [-2.01321349]\n",
      " [ 3.38088086]\n",
      " [-3.50694303]\n",
      " [-4.81478089]\n",
      " [-5.14065751]\n",
      " [-1.72671585]\n",
      " [ 3.44941134]\n",
      " [ 4.89597679]\n",
      " [ 0.45400828]\n",
      " [ 1.85102736]\n",
      " [ 1.20628757]\n",
      " [ 3.17973093]\n",
      " [ 3.36019084]\n",
      " [ 3.96958091]\n",
      " [ 2.01419842]\n",
      " [-4.53142418]\n",
      " [-8.19033235]\n",
      " [ 6.19516948]\n",
      " [-6.46616699]\n",
      " [-2.14793242]\n",
      " [-6.54972571]\n",
      " [-1.40425556]\n",
      " [ 1.68677178]\n",
      " [ 1.05915446]\n",
      " [-6.2766306 ]\n",
      " [-4.67346504]\n",
      " [ 4.57857358]\n",
      " [ 4.57914955]\n",
      " [ 7.4023965 ]\n",
      " [ 7.30130103]\n",
      " [ 5.16988116]\n",
      " [-5.56108899]\n",
      " [-4.03708942]\n",
      " [ 6.90386047]\n",
      " [ 0.60160478]\n",
      " [ 6.4008462 ]\n",
      " [ 5.48025502]\n",
      " [-2.01963366]\n",
      " [ 3.95679895]\n",
      " [-5.16929274]\n",
      " [-1.47670046]\n",
      " [ 6.09149429]\n",
      " [-3.37028962]\n",
      " [ 4.25047203]\n",
      " [-5.70478278]\n",
      " [ 5.9315968 ]\n",
      " [ 5.95909455]\n",
      " [ 8.21667004]\n",
      " [-1.9539825 ]\n",
      " [-6.47332749]\n",
      " [ 6.02342821]\n",
      " [-2.8042808 ]\n",
      " [ 1.63122794]\n",
      " [ 1.7514109 ]\n",
      " [ 5.47082209]\n",
      " [-2.02944197]\n",
      " [-1.8265823 ]\n",
      " [-7.23146853]\n",
      " [-3.88080719]\n",
      " [ 4.52025864]\n",
      " [ 3.71202578]\n",
      " [-4.78772412]\n",
      " [-5.52047074]\n",
      " [ 7.24443871]\n",
      " [ 5.35133818]\n",
      " [-1.8479293 ]\n",
      " [ 0.85933211]\n",
      " [ 0.68376401]\n",
      " [ 0.50121422]\n",
      " [ 3.19793458]\n",
      " [ 2.52791064]\n",
      " [ 7.81186135]\n",
      " [ 8.13670675]\n",
      " [-1.90031536]\n",
      " [-1.55557351]\n",
      " [ 5.22888207]\n",
      " [ 7.11225916]\n",
      " [ 4.32268595]\n",
      " [ 5.44348923]\n",
      " [-1.26778215]\n",
      " [-3.91010112]\n",
      " [ 7.31854344]\n",
      " [ 4.20387187]\n",
      " [ 7.31745031]\n",
      " [ 4.20277875]\n",
      " [ 8.3187305 ]\n",
      " [ 5.99498676]\n",
      " [-4.77974871]\n",
      " [-4.02927988]\n",
      " [-6.44029712]\n",
      " [-4.75738503]\n",
      " [-5.55308819]\n",
      " [-3.37748265]\n",
      " [-2.76752224]\n",
      " [-0.56654335]\n",
      " [ 4.13399421]\n",
      " [ 4.33020379]\n",
      " [ 5.91508215]\n",
      " [ 6.74572914]\n",
      " [-3.30844902]\n",
      " [-2.07793483]\n",
      " [ 2.21870094]\n",
      " [ 3.03231629]\n",
      " [ 6.64861925]\n",
      " [ 6.92533487]\n",
      " [ 4.4970133 ]\n",
      " [ 3.50439872]\n",
      " [ 0.9986072 ]\n",
      " [ 0.96707225]\n",
      " [ 1.08420132]\n",
      " [ 0.06291137]\n",
      " [-6.48015493]\n",
      " [-5.64764216]\n",
      " [-3.79333543]\n",
      " [-3.72786563]\n",
      " [-6.48015493]\n",
      " [-5.64764216]\n",
      " [ 5.58887613]\n",
      " [ 6.15875705]\n",
      " [-2.81483302]\n",
      " [-4.60711028]\n",
      " [-0.99290124]\n",
      " [-0.72573039]\n",
      " [-7.4837424 ]\n",
      " [-7.00112483]\n",
      " [ 2.00057199]\n",
      " [ 1.42016434]\n",
      " [ 2.19348305]\n",
      " [ 1.83733068]\n",
      " [-1.63181358]\n",
      " [-2.30470764]\n",
      " [ 2.05897903]\n",
      " [ 0.87581444]\n",
      " [-1.23150196]\n",
      " [-1.50062441]\n",
      " [-0.65610512]\n",
      " [-0.92522757]\n",
      " [-7.2129019 ]\n",
      " [-7.18385863]\n",
      " [-7.51593825]\n",
      " [-7.29689198]\n",
      " [-0.58574362]\n",
      " [-0.84005043]\n",
      " [ 1.19568948]\n",
      " [ 1.43566489]\n",
      " [ 0.36051293]\n",
      " [-0.54889563]\n",
      " [ 3.89420925]\n",
      " [ 3.59272643]\n",
      " [-0.28775264]\n",
      " [ 0.50026618]\n",
      " [ 5.10395802]\n",
      " [ 4.40789581]\n",
      " [ 3.13261662]\n",
      " [ 1.98320288]\n",
      " [-2.99591183]\n",
      " [-1.1818815 ]\n",
      " [ 5.62088692]\n",
      " [ 3.54669689]\n",
      " [-4.52218414]\n",
      " [-4.90198833]\n",
      " [-3.37972322]\n",
      " [-4.44557838]\n",
      " [-4.52180229]\n",
      " [-4.90250549]\n",
      " [ 1.68119243]\n",
      " [ 2.35647454]\n",
      " [-1.66308832]\n",
      " [-1.85859826]\n",
      " [-3.38442557]\n",
      " [-3.66393599]\n",
      " [ 1.66233641]\n",
      " [ 2.34150197]\n",
      " [ 4.34024072]\n",
      " [ 4.09107454]\n",
      " [ 1.12660139]\n",
      " [ 0.81550542]\n",
      " [-7.49721833]\n",
      " [-8.47815957]\n",
      " [ 3.53935633]\n",
      " [ 4.32345672]\n",
      " [ 4.75568087]\n",
      " [ 4.87886396]\n",
      " [ 4.45959255]\n",
      " [ 5.55448174]\n",
      " [ 4.34102126]\n",
      " [ 5.40620137]\n",
      " [ 7.83687293]\n",
      " [ 8.21298652]\n",
      " [ 0.12394777]\n",
      " [ 0.04848768]\n",
      " [ 6.47054827]\n",
      " [ 5.47249136]\n",
      " [ 7.54313553]\n",
      " [ 7.21807778]\n",
      " [-7.89633687]\n",
      " [-7.42761241]\n",
      " [ 0.01566586]\n",
      " [ 0.74967457]\n",
      " [ 1.57253451]\n",
      " [ 1.97161417]\n",
      " [ 4.84836223]\n",
      " [ 5.96045658]\n",
      " [-7.8029632 ]\n",
      " [-7.60201094]\n",
      " [ 8.11687455]\n",
      " [ 8.21298652]\n",
      " [ 7.37796515]\n",
      " [ 9.06156795]\n",
      " [ 3.0173137 ]\n",
      " [ 3.99083657]\n",
      " [ 2.62302697]\n",
      " [ 2.50540012]\n",
      " [ 6.76451752]\n",
      " [ 6.11019448]\n",
      " [ 3.59817403]\n",
      " [ 4.15785073]\n",
      " [-7.17015972]\n",
      " [-6.43324589]\n",
      " [ 4.07554242]\n",
      " [ 4.04051143]\n",
      " [ 6.95553825]\n",
      " [ 7.01490107]\n",
      " [ 4.74161957]\n",
      " [ 5.07392339]\n",
      " [ 4.63387036]\n",
      " [ 3.92762863]\n",
      " [ 4.59930078]\n",
      " [ 4.27340838]\n",
      " [ 5.76246395]\n",
      " [ 5.56550398]\n",
      " [ 5.31550397]\n",
      " [ 4.65963623]\n",
      " [ 3.74207364]\n",
      " [ 3.93442073]\n",
      " [ 6.10727146]\n",
      " [ 5.59215997]\n",
      " [ 7.58717618]\n",
      " [ 6.77416219]\n",
      " [-0.97127847]\n",
      " [-1.0083062 ]\n",
      " [ 0.36205075]\n",
      " [-0.43844109]\n",
      " [ 3.81974308]\n",
      " [ 2.65662919]\n",
      " [ 4.59763427]\n",
      " [ 3.93931893]\n",
      " [ 7.7375189 ]\n",
      " [ 7.16028984]\n",
      " [ 6.47070111]\n",
      " [ 5.90645173]\n",
      " [ 6.32683232]\n",
      " [ 6.13851772]\n",
      " [ 6.45969273]\n",
      " [ 6.70386904]\n",
      " [ 5.2008352 ]\n",
      " [ 6.28682519]\n",
      " [-4.63134682]\n",
      " [-4.96573033]\n",
      " [ 6.00886922]\n",
      " [ 5.57995314]\n",
      " [ 6.25911855]\n",
      " [ 6.89626117]\n",
      " [-8.08693856]\n",
      " [-8.05116253]\n",
      " [-0.01102971]\n",
      " [ 0.62827298]\n",
      " [-3.96037042]\n",
      " [-3.2946912 ]]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=1)   # 设置保留下来的特征个数为1\n",
    "pca.fit(z2_data)\n",
    "z3_data = pca.fit_transform(z2_data) # 用data_array训练PCA模型，计算，同时返回降维后的数据.\n",
    "vecs = pca.components_.T   # vecs是特征向量，求出一个特征向量作为基\n",
    "\n",
    "print(pca.explained_variance_ratio_)  # 输出贡献率\n",
    "print(z3_data/10)  # 输出数据，与前方的计算结果比较（sklearn会有取反的操作，不影响）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6),dpi=80) \n",
    "plt.figure(1,(8,8)) #设置画图窗口的大小\n",
    "plt.scatter(z2_data[:,0]/10,z2_data[:,1]/10,label='Origin Data') #画出数据集的点\n",
    "\n",
    "\n",
    "xl = np.array([vecs[:,0]*-1,vecs[:,0]]) # vecs，取反是为了和正的构成两个点，绘制直线\n",
    "xl = (xl+z3_data.mean(0)*10) # 得到去中心化的还原数据\n",
    "plt.plot(xl[:,0],xl[:,1],label = 'Linear Regression Function') # xl代表向量，分离出向量，用plot画图->最终需要的线性函数\n",
    "\n",
    "_xtick_labels = [i for i in range(-10,10)] \n",
    "plt.xticks(_xtick_labels[::3])  # 隔3个单位取在x轴显示一次\n",
    "# 画出x轴,y轴\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.legend() # 添加图例，显示该label的内容\n",
    "plt.show() # 显示图像"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 这样整体分析不明显"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raisedhands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Raisedhands\n",
       "0             15\n",
       "1             20\n",
       "2             10\n",
       "3             30\n",
       "4             40\n",
       "..           ...\n",
       "475            5\n",
       "476           50\n",
       "477           55\n",
       "478           30\n",
       "479           35\n",
       "\n",
       "[480 rows x 1 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.iloc[:,2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Raisedhands  Class\n",
       "0             15      1\n",
       "1             20      1\n",
       "2             10      2\n",
       "3             30      2\n",
       "4             40      1\n",
       "..           ...    ...\n",
       "475            5      2\n",
       "476           50      1\n",
       "477           55      1\n",
       "478           30      2\n",
       "479           35      2\n",
       "\n",
       "[480 rows x 2 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(df,columns=['Raisedhands','Class'])\n",
    "replaceMap = {'M':1,'L':2,'H':3}\n",
    "# X = X.map(replaceMap)\n",
    "X['Class'] = X['Class'].map(replaceMap)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 480 entries, 0 to 479\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype\n",
      "---  ------       --------------  -----\n",
      " 0   Raisedhands  480 non-null    int64\n",
      " 1   Class        480 non-null    int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 27.4+ KB\n"
     ]
    }
   ],
   "source": [
    "X.info()   # info看具体情况，25个数据 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raisedhands</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.775000</td>\n",
       "      <td>1.856250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.779223</td>\n",
       "      <td>0.846312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Raisedhands       Class\n",
       "count   480.000000  480.000000\n",
       "mean     46.775000    1.856250\n",
       "std      30.779223    0.846312\n",
       "min       0.000000    1.000000\n",
       "25%      15.750000    1.000000\n",
       "50%      50.000000    2.000000\n",
       "75%      75.000000    3.000000\n",
       "max     100.000000    3.000000"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()   #  个数、平均数、标准差、最小值、下次分位数、中位数、上次分位数、最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_columns = ['Raisedhands']\n",
    "label_column = ['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = X[feature_columns]\n",
    "label = X[label_column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raisedhands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Raisedhands\n",
       "0           15\n",
       "1           20\n",
       "2           10\n",
       "3           30\n",
       "4           40"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()  #  把学习时间变features，成绩变label，都是dataframe类型，再把值赋给x、y，再变数组类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class\n",
       "0      1\n",
       "1      1\n",
       "2      2\n",
       "3      2\n",
       "4      1"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = features.values\n",
    "Y = label.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 1)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape     # 变480行1列的向量  （由于是dataframe类型所以有索引，后面做到去掉索引保留数组）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  #  拆分数据，四分之三的数据作为训练集，四分之一的数据作为测试集（以前传统的如此）\n",
    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression  #  用训练集的数据进行训练\n",
    "regressor = LinearRegression()\n",
    "regressor = regressor.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = regressor.predict(X_test)   #  对测试集进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "# 散点图：红色点表示训练集的点\n",
    "plt.scatter(X_train , Y_train, color = 'red')\n",
    "# 线图：蓝色线表示由训练集训练出的线性回归模型\n",
    "plt.plot(X_train , regressor.predict(X_train), color ='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点图：红色点表示测试集的点\n",
    "plt.scatter(X_test , Y_test, color = 'red')\n",
    "# 线图：蓝色线表示对测试集进行预测的结果\n",
    "plt.plot(X_test , regressor.predict(X_test), color ='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，单纯地以举手次数和学习成绩分析，并不能得出其相关性，由于舍弃太多特征值，所以为达到预想的效果，只能返回前面，选择其中重要性大的特征值，然后用PCA进行降维处理，可以尝试降维到2维，然后再进行分析（前文已处理完毕）。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "6e8eef4521a2bf3a3f21962056866533a6eb29592c29faf5baf08f52dd46a3f6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
